Apparatus for encrypting external communication for an electric aircraft

ABSTRACT

In an aspect an apparatus for encrypting external communication for an electric aircraft. The apparatus may comprise a communication component configured to communicate with a network node. The apparatus may also include a battery pack configured to power the electric aircraft and a battery sensor, wherein a battery senor is configured to generate battery datum. A computing device may be included within the apparatus. The computing device may be configured to be receive the battery datum, encrypt the battery datum using an encryption process, identify the network node and transmit the encrypted battery datum to the network node using the communication component.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricvehicles. In particular, the present invention is directed to anapparatus for encrypting external communication for an electricaircraft.

BACKGROUND

In the operation of an electric aircraft, communication between thepilot of the electric aircraft and ground support. A reliable networkmay aid in connecting the pilot to ground support.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for encrypting external communication for anelectric aircraft. The apparatus may comprise a communication componentconfigured to communicate with a network node. The apparatus may alsoinclude a battery pack configured to power the electric aircraft and abattery sensor, wherein a battery senor is configured to generatebattery datum. A computing device may be included within the apparatus.The computing device may be configured to be receive the battery datum,encrypt the battery datum using an encryption process, identify thenetwork node and transmit the encrypted battery datum to the networknode using the communication component.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram depicting an apparatus for encrypting externalcommunication for an electric aircraft;

FIG. 2 is a block diagram of an exemplary machine-learning process;

FIG. 3 is a schematic of an exemplary electric aircraft;

FIG. 4 is a diagrammatic representation of an exemplary s of a batterymodule;

FIG. 5 is a block diagram of an exemplary embodiment of anauthentication module;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anauthentication database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aphysical signature database;

FIG. 8 is a schematic of an exemplary sensor suite;

FIG. 9 is a block diagram of an exemplary flight controller;

FIG. 10 is a schematic of an exemplary avionic mesh network;

FIG. 11 is a block diagram of an exemplary method of use for anapparatus for encrypting external communication for an electricaircraft; and

FIG. 12 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to anapparatus for encrypting external communication for an electricaircraft. The apparatus may comprise a communication componentconfigured to generate a communication link with ground support. Theapparatus may also include a battery pack configured to power theelectric aircraft and a battery sensor, wherein a battery senor isconfigured to generate battery datum. A computing device may be includedwithin the apparatus. The computing device may be configured to bereceive the battery datum, encrypt the battery datum using an encryptionprocess, and transmit the battery datum using the communicationcomponent. Exemplary embodiments illustrating aspects of the presentdisclosure are described below in the context of several specificexamples.

Referring now to FIG. 1 , an exemplary embodiment of an apparatus 100for an apparatus for encrypting external communication for an electricaircraft is illustrated. System includes a computing device 104.computing device 104 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. computing device 104 may include a singlecomputing device operating independently, or may include two or morecomputing device operating in concert, in parallel, sequentially or thelike; two or more computing devices may be included together in a singlecomputing device or in two or more computing devices. computing device104 may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting computing device104 to one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.computing device 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. computing device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. computing device 104 may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. computing device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

With continued reference to FIG. 1 , computing device 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, computingdevice 104 may be configured to perform a single step or sequencerepeatedly until a desired or commanded outcome is achieved; repetitionof a step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. computing device 104 may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which steps, sequencesof steps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Still referring to FIG. 1 , apparatus 100 may include an electricaircraft. In a non-limiting embodiment, the electric aircraft mayinclude an electric vertical take-off and landing (eVTOL) aircraft, adrone, an unmanned aerial vehicle (UAV), etc. In a non-limitingembodiment, apparatus 100 may include electric aircraft 108, whereinelectric aircraft 108 is configured to transmit their respectiveaircraft data or battery datum 112 to computing device 104. An “aircraftdata,” for the purpose of this disclosure, is a collection ofinformation generated by an electric aircraft describing any informationinvolving the electric aircraft and/or captured by the electricaircraft. In a non-limiting embodiment, aircraft data may include acomponent state data. A “component state data,” for the purposes of thisdisclosure, is an element of data describing the status or health statusof a flight component or any component of an electric aircraft. A“flight component,” for the purposes of this disclosure, includescomponents related to, and mechanically connected to an aircraft thatmanipulates a fluid medium in order to propel and maneuver the aircraftthrough the fluid medium. The operation of the aircraft through thefluid medium will be discussed at greater length hereinbelow. Thecomponent state data may include information such as, but not limitedto, an aircraft flight duration, a distance of the aircraft flight, aplurality of distances of an aircraft from the surface, and the like.The component state data may denote a location of the aircraft, statusof the aircraft such as health and/or functionality, aircraft flighttime, aircraft on frame time, and the like thereof. In a non-limitingembodiment, component state data may include aircraft logistics of anelectric aircraft of a plurality of electrical aircraft. An “aircraftlogistics,” for the purposes of this disclosure, refer to a collectionof datum representing any detailed organization and implementation of anoperation of an electric aircraft. In a non-limiting embodiment,aircraft logistics may include unique identification numbers assigned toeach electric aircraft. In a non-limiting embodiment, aircraft logisticsmay include a historical record of locations corresponding to anelectric aircraft that may represent the aircraft's destination orpotential destination. Aircraft logistics may include time an electricaircraft was in the air and a historical record of the different rate ofvelocity the aircraft may have commanded. In a non-limiting embodiment,the component state data may include a history of health information ofan electric aircraft. In a non-limiting embodiment, a history of anelectric aircraft's health may be measured with the ability to bepresented in a visual format to a user.

With continued reference to FIG. 1 , apparatus 100 may include a network116 configured to connect the electric aircraft 108 to ground controland other electric aircrafts and communicate with each other as afunction of computing device 104. A “network,” for the purpose of thisdisclosure, is any medium configured to facilitate communication betweentwo or more devices. Network 116 may include any mesh network describedin this disclosure, for example without limitation an avionic meshnetwork. For instance and without limitation, the avionic mesh networkmay be consistent with the avionic mesh network in U.S. patentapplication Ser. No. 17/348,916 and entitled “METHODS AND SYSTEMS FORSIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL)AIRCRAFT,” which is incorporated by reference herein in its entirety. Ina non-limiting embodiment, network 116 may include a central meshnetwork and a plurality of local mesh networks. A “central meshnetwork,” as used in this disclosure, is a mesh network used by electricaircrafts, wherein each node of the central mesh network includes anentity that is associated with the fleet. Any mesh network may include acomputing device configured to generate nodes to its mesh network. In anon-limiting embodiment, each node of the central mesh network mayinclude any electric aircraft of the same fleet and any entity such as,but not limited to, a ground support 124 associated with the fleet, afleet manager of the fleet of electric aircrafts operating a remotedevice, and the like thereof. A “local mesh network,” as used in thisdisclosure, is a mesh network created by the computing device of anelectric aircraft of the fleet, wherein the electric aircraft is thecentral node of its local mesh network. In a non-limiting embodiment,each electric aircraft may be the central node if its respective localmesh network. This is so, at least in part, because an electric aircraftof the fleet may detect other entities not associated with the fleetsuch as, but not limited to, other aircrafts, an air traffic controlauthority, and the like thereof, that the central mesh network of thefleet may not be in range of detecting the other entities. The centralmesh network and/or the local mesh network may include some securityprogram such as authentication module 136 to authorize some level ofcommunication between the electric aircraft and the other entities. In anon-limiting embodiment, the central mesh network may authenticate theother entities and generate additional nodes into the central meshnetwork temporarily. In another non-limiting embodiment, the centralmesh network may merge with the plurality of local mesh networks.Alternatively or additionally, the central mesh network may be a mergeof the plurality of local mesh networks. In some embodiments, thecentral mesh network may generate the additional nodes and integratethem into the central mesh network and delete those nodes. The centralmesh network may only temporarily generate the additional nodes to allowfor any data the central mesh network may have to be sent over to theother entities via the additional nodes. The central mesh network maythen delete those nodes once communication is complete. The central meshnetwork may include a central node, which may be a ground stationassociated with the fleet and/or a fleet manager, wherein the range ofthe central mesh network originates from the position of the centralnode. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various levels of access of nodes and datafor purposes as described herein.

In a non-limiting embodiment, network 116 may be configured to identifyany nearby electric aircraft. Network 116 and/or computing device 104may be configured to identify if the nearby electric aircraft is part ofthe fleet of electric aircrafts associated with computing device 104and/or network 116 via an authentication module 136. An “authenticationmodule,” for the purpose of this disclosure, is a hardware and/orsoftware module configured to authenticate an electric aircraft and/oruser associated with the electric aircraft. In a non-limitingembodiment, computing device 104 may be configured to establish aconnection with between the plurality of electric aircrafts of theelectric aircraft fleet, via network 116 or any radio frequency orBluetooth connection using authentication module 136. In a non-limitingembodiment, authentication may be performed automatically viaauthentication module 136. In a non-limiting embodiment, authenticationmay be performed manually by a fleet manager using a remote user devicecomprising computing device 104. A “fleet manager,” for the purpose ofthis disclosure, is an authoritative figure configured to monitor,manage, and/or supervise the network communication of an electricaircraft fleet assigned to the fleet manager. A “remote user device,”for the purpose of this disclosure, is a computing device that includesan interactive device and graphical user interface (GUI). The remoteuser device may be used as an interactive platform that may providevisualization of the fleet communication and aircraft data 108 beingtransferred. The remote user device may be used to monitor and verifyadditional electric aircrafts of the fleet into network 116. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the management of the electric aircraft fleet communicationby a fleet manager for authentication purposes as described herein.

Still referring to FIG. 1 , apparatus 100 may include communicationcomponents 120. “Communication components” as used in this disclosureare any devices capable of receiving and transmitting data. In anon-limiting embodiment, the communication components may include atransceiver. For example and without limitation, communicationcomponents 120 may be configured to transfer transmissions signalsdescribing battery datum 112 to each other. Each communication componentmay be assigned to an electric aircraft or ground support. In anon-limiting embodiment, computing device 104 may include a plurality ofcommunication components for each electric aircraft of the fleet or eachground support station. In a non-limiting embodiment, only some of theelectric aircraft or ground support 124 stations may be online and/orcommunicating via network 116, in which only the connected electricaircrafts and ground support station and their associated communicationcomponents may be active in the communication process.

Still referring to FIG. 1 , the communication components may include aphysical CAN bus unit and/or virtual CAN bus unit. For example andwithout limitation, each communication component may receivetransmission signals comprising of aircraft data 108 from a physical CANbus unit of the electric aircraft the communication component isreceiving from. For instance, if electric aircraft 108 wants tocommunicate and/or transmit data to ground support, electric aircraft108 may transmit battery data to communication component 120, which maytransfer transmission signals of battery datum 112 to ground support 12In a non-limiting embodiment, communication component 120 comprising aphysical CAN bus unit may transmit the transmission signals containingbattery datum 112 to a physical CAN bus unit of communication component120. Alternatively or additionally communication component 120comprising a virtual CAN bus unit may transmit the transmission signalscontaining battery datum 112 to a virtual CAN bus unit of communicationcomponent 120.

Still referring to FIG. 1 , computing device 104 may use communicationcomponent 120 to generate various networking systems and/or layers. In anon-limiting embodiment, computing device 104 may include an automatedbroadcaster configured to determine the location of each electricaircraft connected within network 116. The automated broadcaster mayinclude an Automatic Dependent Surveillance-Broadcast (ADS-B) whichincludes a surveillance technology in which a simulated vehicle maydetermine the position of the simulated vehicle of its respectedsimulation device. In a non-limiting embodiment, computing device 104may be configured to communicate with an air traffic control (ATC)operator and or pilots of other electric aircrafts for flight planpurpose. For example and without limitation, the data from automatedbroadcaster can also be received by other aircrafts to providesituational awareness and allow self-separation. In a non-limitingembodiment, ADS-B is “automatic” in that it requires no pilot orexternal input. It is “dependent” in that it depends on data from theaircraft's navigation system. In a non-limiting embodiment, theautomated broadcaster may be configured to be a hub for digitalcommunication with at least a simulated air traffic control operator ofthe simulated air traffic control. For instance and without limitation,the communication component may be consistent with the communicationcomponent in U.S. patent application Ser. No. 17/574,919 and entitled“SYSTEMS AND METHODS FOR SWARM COMMUNICATION FOR AN ELECTRIC AIRCRAFTFLEET,” which is incorporated by reference herein in its entirety.

With continued reference to FIG. 1 , computing device 104 may include aplurality of physical controller area network buses. A “physicalcontroller area network bus,” as used in this disclosure, is vehicle busunit including a central processing unit (CPU), a CAN controller, and atransceiver designed to allow devices to communicate with each other'sapplications without the need of a host computer which is locatedphysically at the aircraft. For instance and without limitation, CAN busunit may be consistent with disclosure of CAN bus unit in U.S. patentapplication Ser. No. 17/218,342 and titled “METHOD AND SYSTEM FORVIRTUALIZING A PLURALITY OF CONTROLLER AREA NETWORK BUS UNITSCOMMUNICATIVELY CONNECTED TO AN AIRCRAFT,” which is incorporated hereinby reference in its entirety. Physical controller area network (CAN) busunit may include physical circuit elements that may use, for instanceand without limitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks; circuit elements maybe used to implement CAN bus components and/or constituent parts asdescribed in further detail below. Physical CAN bus unit may includemultiplex electrical wiring for transmission of multiplexed signaling.Physical CAN bus unit may include message-based protocol(s), wherein theinvoking program sends a message to a process and relies on that processand its supporting infrastructure to then select and run appropriateprograming. In a non-limiting embodiment, computing device 104 mayinclude a plurality of physical CAN bus units wherein each physical CANbus unit is configured to receive an aircraft data from an electricaircraft, wherein each physical CAN bus unit is associated withreceiving datum from that specific electric aircraft. In someembodiments, computing device 104 may assign a physical CAN bus unit toa unique electric aircraft of the fleet.

Still referring to FIG. 1 , computing device 104 may include a pluralityof controller area network gateways connected to the plurality ofphysical CAN bus units. A “controller area network gateway,” as used inthis disclosure, is a piece of networking hardware used for transmissionof data signals from one discrete network to another. In a non-limitingembodiment, the CAN gateways may include routers and/or switches whichmay provide interoperability between physical CAN bus unitscommunicatively connected with the electric aircrafts and switches, suchas Ethernet switches, wherein the intraoperatively may include thetransmission of battery datum 112 between the electric aircraft and theEthernet switch. In a non-limiting embodiment, computing device 104 mayinclude at least a network switch communicatively connected to theplurality of controller area network gateways configured to receive thetransmitted measured state data and transmit the measured state data viaa transmission signal. A “network switch,” as used in this disclosure,is a networking hardware that connects devices on a computer networkusing packet switching to receive and forward data to a destinationdevice. A network switch may include an Ethernet hub switch, which maybe used for Fiber Channel.

Continuing in reference to FIG. 1 , a transmission signal of batterydatum 112 from a physical CAN bus unit located at aircraft may betransmitted to a virtual CAN bus, and/or virtual CAN bus unit. Forinstance and without limitation, the virtual CAN bus unit may beconsistent with the virtual CAN bus unit in U.S. patent application Ser.No. 17/218,342. In a non-limiting embodiment, computing device 104 mayadditionally include or be configured to perform operations functioninga virtual controller area network. virtual CAN bus unit may beconfigured to demultiplex an incoming transmission signal into aplurality of outgoing messages originating from the plurality ofphysical controller area network buses. Demultiplexing may includeprocesses of reconverting a transmission signal containing, for examplecontaining multiple analogue and/or digital signal streams from atelectric aircraft 108 and/or computing device 104, back into originalseparate and unrelated signals originally relayed from controller areanetwork. Demultiplexing may include extracting original channels on areceiving end to identify which physical CAN bus unit a signaloriginates from. Demultiplexing may be performed using a demultiplexersuch as a binary decoder, or any programmable logic device.Demultiplexing may be performed using a computing software operating onthe virtual CAN bus unit, which may deconvolute a signal.

With continued reference to FIG. 1 , the term ‘battery’ is used as acollection of cells connected in series or parallel to each other. Abattery cell 128 may, when used in conjunction with other cells, may beelectrically connected in series, in parallel or a combination of seriesand parallel. Series connection comprises wiring a first terminal of afirst cell to a second terminal of a second cell and further configuredto comprise a single conductive path for electricity to flow whilemaintaining the same current (measured in Amperes) through any componentin the circuit. A battery cell 128 may use the term ‘wired’, but one ofordinary skill in the art would appreciate that this term is synonymouswith ‘electrically connected’, and that there are many ways to coupleelectrical elements like battery cells 128 together. An example of aconnector that do not comprise wires may be prefabricated terminals of afirst gender that mate with a second terminal with a second gender.Battery cells 128 may be wired in parallel. Parallel connectioncomprises wiring a first and second terminal of a first battery cell toa first and second terminal of a second battery cell and furtherconfigured to comprise more than one conductive path for electricity toflow while maintaining the same voltage (measured in Volts) across anycomponent in the circuit. Battery cells 128 may be wired in aseries-parallel circuit which combines characteristics of theconstituent circuit types to this combination circuit. Battery cells 128may be electrically connected in a virtually unlimited arrangement whichmay confer onto the system the electrical advantages associated withthat arrangement such as high-voltage applications, high-currentapplications, or the like. In an exemplary embodiment, Battery module128 comprise 196 battery cells in series and 18 battery cells inparallel. This is, as someone of ordinary skill in the art wouldappreciate, is only an example and Battery module 104 may be configuredto have a near limitless arrangement of battery cell configurations. Forinstance and without limitation, the battery may be consistent with thebattery in U.S. patent application Ser. No. 17/564,305 and entitled“SYSTEM FOR TRANSMITTING BATTERY PACK DATA OF AN ELECTRIC AIRCRAFT ANDMETHOD FOR ITS USE,” which is incorporated by reference herein in itsentirety.

With continued reference to FIG. 1 , a plurality of battery modules 128may also comprise a side wall which comprises a laminate of a pluralityof layers configured to thermally insulate the plurality of batterycells 128 from external components of battery module 128. Side walllayers may comprise materials which possess characteristics suitable forthermal insulation as described in the entirety of this disclosure likefiberglass, air, iron fibers, polystyrene foam, and thin plastic films,to name a few. Side wall may additionally or alternatively electricallyinsulate the plurality of battery cells 128 from external components ofbattery module and the layers of which may comprise polyvinyl chloride(PVC), glass, asbestos, rigid laminate, varnish, resin, paper, Teflon,rubber, and mechanical lamina. Center sheet may be mechanically coupledto side wall in any manner described in the entirety of this disclosureor otherwise undisclosed methods, alone or in combination. Side wall maycomprise a feature for alignment and coupling to center sheet. Thisfeature may comprise a cutout, slots, holes, bosses, ridges, channels,and/or other undisclosed mechanical features, alone or in combination.Plurality of battery module may be a combination of a plurality ofbattery module 128 utilized to power the electric aircraft. Batterymodule may include any of the batteries described in U.S. Nonprovisionalapplication Ser. No. 16/948,140, filed on Sep. 4, 2020, and entitled“SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE”, the entiretyof which is incorporated herein by reference.

With continued reference to FIG. 1 , at least a battery sensor 132 isconfigured to detect battery datum 112. For the purposes of thisdisclosure, a “battery datum” is an electronic signal representing anelement of information and/or a parameter of a detected electricaland/or physical characteristic and/or phenomenon correlated with a stateof a battery. Battery datum 112 may include but is not limited tobattery temperature, battery health, battery life cycle, batterycapacity, battery discharge rate, battery charge cycle, battery maximumcapacity, battery remaining capacity, and the like. Battery datum 112may additionally include any information describing the state of thebattery pack.

Still referring to FIG. 1 , as used in this disclosure, a “sensor” is adevice that is configured to detect a phenomenon and transmitinformation related to the detection of the phenomenon electronically.For example, in some cases a sensor may transduce a detected phenomenon,such as without limitation, voltage, current, speed, direction, force,torque, resistance, moisture temperature, pressure, and the like, into asensed signal. Sensor may include one or more sensors which may be thesame, similar, or different. Sensor may include a plurality of sensorswhich may be the same, similar, or different. Sensor may include one ormore sensor suites with sensors in each sensor suite being the same,similar, or different.

Still referring to FIG. 1 , battery sensor 132 may include any number ofsuitable sensors which may be efficaciously used to detect battery datum112. For example, and without limitation, these sensors may include avoltage sensor, current sensor, multimeter, voltmeter, ammeter,electrical current sensor, resistance sensor, impedance sensor,capacitance sensor, a Wheatstone bridge, displacements sensor, vibrationsensor, Daly detector, electroscope, electron multiplier, Faraday cup,galvanometer, Hall effect sensor, Hall probe, magnetic sensor, opticalsensor, magnetometer, magnetoresistance sensor, MEMS magnetic fieldsensor, metal detector, planar Hall sensor, thermal sensor, and thelike, among others. Battery sensor 132 may efficaciously include,without limitation, any of the sensors disclosed in the entirety of thepresent disclosure

With continued reference to FIG. 1 , battery datum 112 may includebattery temperature. As used in the current disclosure, “batterytemperature” is the temperature of the battery at a given time. In someembodiments, Battery temperature may include the ideal temperature ofthe battery. In other embodiments, battery temperature may include thecurrent temperature of the battery. Battery temperature may includepre-flight battery temperature and post charging battery temperature. Asused in this disclosure, a “pre-flight battery temperature” is atemperature a battery is to be set to before the electric aircraft takesoff. As used in this disclosure, “post-charging battery temperaturedatum” is datum related to and/or indicating a temperature of a batteryduring a charging process or shortly after the charging process iscomplete. Battery temperature may also include a comparison between thepre-flight battery temperature and the post-charging batterytemperature.

With continued reference to FIG. 1 , battery datum 112 may includebattery health. As used in the current disclosure, a “battery healthdatum” is a datum indicative of an overall state of health of thebattery. The state of health of the battery may be measured by comparingthe batteries current state of health against the batteries state ofhealth at the time it was manufactured. The state of health of thebattery may take into account internal resistance, capacity, voltage,self-discharge, ability to accept a charge, number of charge-dischargecycles, age of the battery, the average temperature of the battery andthe like.

With continued reference to FIG. 1 , battery datum 112 may includebattery life cycle datum. As used in the current disclosure, “batterylife cycle datum” is a datum regarding the batteries charge cycle. Acharge cycle is the process of charging a rechargeable battery anddischarging it as required into a load. In general, number of cycles fora rechargeable battery indicates how many times it can undergo theprocess of complete charging and discharging until failure or itstarting to lose capacity. In embodiments, battery life cycle datum maybe used to estimate when the battery needs to be replaced. In otherembodiments, battery life cycle datum maybe used to estimate how muchcharge a battery will be able to hold. A determination of state ofcharge (SOC) may be used to determine the battery life cycle datum. As anon-limiting example, the power and current draws may be fromenvironmental conditions, components of the energy source or otherfactors which impact the energy source state of charge (SOC). SOC, asused herein, is a measure of remaining capacity as a function of timeand is described in more detail below. SOC and/or maximum power thebattery 104 can deliver may decrease during flight as the voltagedecreases during discharge. SOC and/or power output capacity of anenergy source may be associated with an ability of the battery todeliver energy as needed for a task such as driving a propulsor for aphase of flight such as landing, hovering, or the like. As anon-limiting example, other factors, including state of voltage, and/orestimates of state of voltage or other electrical parameters of anenergy source, may be used to estimate current state of a battery 128and/or future ability to deliver power and/or energy. Certaincalculations of battery life cycle datum, state of charge, and state ofvoltage which may efficaciously be utilized in accordance with certainembodiments of the present disclosure are disclosed in U.S.Nonprovisional application Ser. No. 17/349,182, filed on Jun. 16, 2021,entitled “SYSTEMS AND METHODS FOR INFLIGHT OPERATION ASSESSMENT,” theentirety of which is incorporated herein by reference.

Still referring to FIG. 1 , computing device 104 may be configured toanalyze battery datum 112. As used in the current disclosure, “Analyzingbattery datum” is the process of systematically applying statisticaland/or logical techniques to describe and illustrate, condense, andrecap, and evaluate battery datum. In embodiments, analyzing batterydatum 112 may consist of taking the raw battery data collected from atleast a sensor and refining it into useful statistics and metricsregarding the electric aircraft. For example, battery datum analysis mayinclude analyzing the batteries life cycle datum and the batterieshealth. Battery datum analysis may also include information about theelectric vehicle.

Still referring to FIG. 1 , computing device 104 may be configured toanalyze battery datum 112 using machine learning. Machine-learningmodule may perform determinations, classification, and/or analysissteps, methods, processes, or the like as described in this disclosureusing machine learning processes. A “machine learning process,” as usedin this disclosure, is a process that automatedly uses training data togenerate an algorithm that will be performed by a computingdevice/module to produce a battery datum analysis given battery dataprovided as inputs. As used in the current disclosure, “training data,”as used herein, is data containing correlations that a machine-learningprocess may use to model relationships between two or more categories ofdata. In some embodiments, the inputs into the machine learning processare a batteries life cycle datum and the batteries health and the outputof the process the battery datum analysis. In a non-limiting example,training data that may be correlated to include battery datum such asinternal resistance, capacity, voltage, self-discharge, ability toaccept a charge, number of charge-discharge cycles, age of the battery,the average temperature of the battery, batteries life cycle datum,batteries health and the like. In some embodiments, training data mayinclude datum recorded previous flights where batteries acted within anoptimal range, did not require modifications to the flight plan due tobattery issues, and did not exceed or drop below a desired temperaturerange. In some embodiments, training data may be generated viaelectronic communication between a computing device and plurality ofsensors. In other embodiments, training data may be communicated to amachine learning model from a remote device. Once the machine learningprocess receives training data, it may be implemented in any mannersuitable for generation of receipt, implementation, or generation ofmachine learning.

Still referring to FIG. 1 , computing device 104 may be configured toanalyze battery datum as a function of a battery's life cycle datum. Inembodiments, battery datum 112 analysis may include analysis of thebatteries life cycle to determine life expectancy of the battery. Thislife expectancy analysis may be averaged with the life span of otherbatteries to create an estimated life expectancy of a battery. In otherembodiments, battery datum analysis may be used to determine thecapacity of the battery to hold a charge. Battery datum analysis maybeelectric vehicles to estimate fleet's life span and maintenance costs.

Still referring to FIG. 1 , computing device 104 may be configured toanalyze battery datum as a function of a battery's health. Inembodiments, battery datum analysis may include an evaluation of batterydatum such as internal resistance, capacity, voltage, self-discharge,ability to accept a charge, number of charge-discharge cycles, age ofthe battery, the average temperature of the battery and the like.Battery datum analysis may compile all the aforementioned variables intoone statistic to determine the overall state of health of the battery.Battery datum analysis compare the current state of health of thebattery to the state of health of the Battery at the time ofmanufacturing

With continued reference to FIG. 1 , the electric aircraft 108 mayencrypt its respective battery datum 112 before transmitting it toanother party such as ground support 124, another electric aircraft 108,and/or computing device 104. Computing device 104 may be configured todecrypt aircraft data 108 received, confirm the identity of the electricaircraft of both the sender and recipient of the aircraft data, whichcould be another electric aircraft, and transmit the aircraft data tothe recipient. For example and without limitation, electric aircraft 108may want to transmit its battery datum 112 to another electric aircraftor ground support 124, in which the transmission is completed throughcomputing device 104 and its communication components 120. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the using encryption and decryption methodologies in thecontext of transferring data between electric aircrafts.

With continued reference to FIG. 1 , apparatus 100 may include a groundsupport 124. As used in the current disclosure, “ground support” is aground-based air traffic controllers who direct aircraft on the groundand through a given section of controlled airspace and can provideadvisory services to aircraft in non-controlled airspace. Ground support124 may include but is not limited to a charging station, landingstation, air traffic control, airports, and the like. IN an embodimentGround support 124 may monitor the location of aircraft in theirassigned airspace by radar and communicate with the pilots by radio. Inother embodiments, Ground Support 124 enforces traffic separation rulesand other FAA rules, which ensure each aircraft always maintains aminimum amount of empty space around it. Ground support 124 may directan aircraft to a charging station as a function of battery datum. Groundsupport 124 may also be in communication with the pilot of an electricaircraft.

With continued reference to FIG. 1 , network 116 may be configured toidentify any nearby ground support 124. Network 116 and/or computingdevice 104 may be configured to identify if the nearby ground support124 or electric aircraft associated with computing device 104 and/ornetwork 116 via an authentication module 136. An “authenticationmodule,” for the purpose of this disclosure, is a hardware and/orsoftware module configured to authenticate an electric aircraft and/oruser associated with the electric aircraft. In a non-limitingembodiment, computing device 104 may be configured to establish aconnection with between the plurality of electric aircrafts of theelectric aircraft fleet, via network 116 or any radio frequency orBluetooth connection using authentication module 136. In a non-limitingembodiment, authentication may be performed automatically viaauthentication module 136. In a non-limiting embodiment, authenticationmay be performed manually by a fleet manager using a remote user devicecomprising computing device 104. A “fleet manager,” for the purpose ofthis disclosure, is an authoritative figure configured to monitor,manage, and/or supervise the network communication of an electricaircraft fleet assigned to the fleet manager. A “remote user device,”for the purpose of this disclosure, is a computing device that includesan interactive device and graphical user interface (GUI). The remoteuser device may be used as an interactive platform that may providevisualization of the fleet communication and aircraft data 108 beingtransferred. The remote user device may be used to monitor and verifyadditional electric aircrafts of the fleet into network 116. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of the management of the electric aircraft fleet communicationby a fleet manager for authentication purposes as described herein.

In a non-limiting embodiment, computing device 104 may be configured tocompare the credential from user device to an authorized credentialstored within an authentication database, and bypass authentication foruser device based on the comparison of the credential from user deviceto the authorized credential stored within the authentication database.A “credential” as described in the entirety of this disclosure, is anydatum representing an identity, attribute, code, and/or characteristicspecific to a user, a user device, and/or an electric aircraft. Forexample and without limitation, the credential may include a usernameand password unique to the user, the user device, and/or the electricaircraft. The username and password may include any alpha-numericcharacter, letter case, and/or special character. As a further exampleand without limitation, the credential may include a digitalcertificate, such as a PKI certificate. The remote user device and/orthe electric aircraft may include an additional computing device, suchas a mobile device, laptop, desktop computer, or the like; as anon-limiting example, the user device may be a computer and/or smartphone operated by a pilot-in-training at an airport hangar. The remoteuser device and/or electric aircraft may include, without limitation, adisplay in communication with computing device 104; the display mayinclude any display as described in the entirety of this disclosure suchas a light emitting diode (LED) screen, liquid crystal display (LCD),organic LED, cathode ray tube (CRT), touch screen, or any combinationthereof. Output data from computing device 104 may be configured to bedisplayed on user device using an output graphical user interface. Anoutput graphical user interface may display any output as described inthe entirety of this disclosure. As a further embodiment, authenticationmodule 136 and/or computing device 104 may be configured to receive acredential from an admin device. The admin device may include anyadditional computing device as described above in further detail,wherein the additional computing device is utilized by/associated withan employee of an administrative body, such as an employee of thefederal aviation administration.

With continued reference to FIG. 1 , apparatus 100 may include a clouddatabase 140 configured to record any record or data that may betransmitted within network 116. A “cloud database,” for the purpose ofthis disclosure, is a data storage system that runs on a cloud computingplatform such as computing device 104. In a non-limiting embodiment,cloud database 140 may store any aircraft data 108 as described herein.In another non-limiting embodiment, cloud database 140 may be used bycomputing device 104 to retrieve any training data for machine-learningpurposes.

Referring now to FIG. 2 , an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 2 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2 ,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 2 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 1616 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 2 , machine-learning module 200 may beconfigured to perform a lazy-learning process 220 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 204. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 204 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 2 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2 , machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 204. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process228 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 2 , machine learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2 , machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Continuing to refer to FIG. 2 , wherein a machine learning model isconfigured to generate analysis as a function of battery datum. Inembodiments, training data for a machine learning model may includebattery datum. Battery datum may also be used as a Training Example fora machine learning process. As used in the current disclosure, a“Training Example” is an example that a machine learning device uses tocorrelate the current example to a similar examples with the goal totrain the machine learning device. Training example may include anyscenario regarding the battery of an aircraft. In a non-limitingexample, a training example may cover failure of the battery duringflight. In other embodiments, a training example may cover an irregulartemperature of the battery. A training example may include training dataand any derivation or calculation stemming from battery datum. Trainingexamples may also include battery life cycle datum and battery healthdatum. A machine learning device may be configured to receive a trainingexample. A machine learning device may be configured to generateanalysis of the battery datum as a function of the training examplesbattery datum.

Referring now to FIG. 3 , an exemplary embodiment of an aircraft 300 isillustrated. Aircraft 300 may include an electrically powered aircraft(i.e., electric aircraft). In some embodiments, electrically poweredaircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. “Rotor-based flight,” as described in thisdisclosure, is where the aircraft generated lift and propulsion by wayof one or more powered rotors coupled with an engine, such as aquadcopter, multi-rotor helicopter, or other vehicle that maintains itslift primarily using downward thrusting propulsors. “Fixed-wing flight,”as described in this disclosure, is where the aircraft is capable offlight using wings and/or foils that generate lift caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

Still referring to FIG. 3 , aircraft 300 may include a fuselage 304. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 304 may comprisestructural elements that physically support the shape and structure ofan aircraft. Structural elements may take a plurality of forms, alone orin combination with other types. Structural elements may vary dependingon the construction type of aircraft and specifically, the fuselage.Fuselage 304 may comprise a truss structure. A truss structure may beused with a lightweight aircraft and may include welded aluminum tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisetitanium construction in place of aluminum tubes, or a combinationthereof. In some embodiments, structural elements may comprise aluminumtubes and/or titanium beams. In an embodiment, and without limitation,structural elements may include an aircraft skin. Aircraft skin may belayered over the body shape constructed by trusses. Aircraft skin maycomprise a plurality of materials such as aluminum, fiberglass, and/orcarbon fiber, the latter of which will be addressed in greater detaillater in this paper.

Still referring to FIG. 3 , aircraft 300 may include a plurality ofactuators 308. Actuator 308 may include any motor and/or propulsordescribed in this disclosure, for instance in reference to FIGS. 1-11 .In an embodiment, actuator 308 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, Hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. As used inthis disclosure an “aircraft” is vehicle that may fly. As a non-limitingexample, aircraft may include airplanes, helicopters, airships, blimps,gliders, paramotors, and the like thereof. In an embodiment, mechanicalcoupling may be used to connect the ends of adjacent parts and/orobjects of an electric aircraft. Further, in an embodiment, mechanicalcoupling may be used to join two pieces of rotating electric aircraftcomponents.

With continued reference to FIG. 3 , a plurality of actuators 308 may beconfigured to produce a torque. As used in this disclosure a “torque” isa measure of force that causes an object to rotate about an axis in adirection. For example, and without limitation, torque may rotate anaileron and/or rudder to generate a force that may adjust and/or affectaltitude, airspeed velocity, groundspeed velocity, direction duringflight, and/or thrust. For example, plurality of actuators 308 mayinclude a component used to produce a torque that affects aircrafts'roll and pitch, such as without limitation one or more ailerons. An“aileron,” as used in this disclosure, is a hinged surface which formpart of the trailing edge of a wing in a fixed wing aircraft, and whichmay be moved via mechanical means such as without limitationservomotors, mechanical linkages, or the like. As a further example,plurality of actuators 308 may include a rudder, which may include,without limitation, a segmented rudder that produces a torque about avertical axis. Additionally or alternatively, plurality of actuators 308may include other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjustmovement of aircraft 300. Plurality of actuators 308 may include one ormore rotors, turbines, ducted fans, paddle wheels, and/or othercomponents configured to propel a vehicle through a fluid mediumincluding, but not limited to air.

Still referring to FIG. 3 , plurality of actuators 308 may include atleast a propulsor component. As used in this disclosure a “propulsorcomponent” or “propulsor” is a component and/or device used to propel acraft by exerting force on a fluid medium, which may include a gaseousmedium such as air or a liquid medium such as water. In an embodiment,when a propulsor twists and pulls air behind it, it may, at the sametime, push an aircraft forward with an amount of force and/or thrust.More air pulled behind an aircraft results in greater thrust with whichthe aircraft is pushed forward. Propulsor component may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. In an embodiment, propulsor component may include a pullercomponent. As used in this disclosure a “puller component” is acomponent that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a puller propulsor, and thelike. Additionally, or alternatively, puller component may include aplurality of puller flight components. In another embodiment, propulsorcomponent may include a pusher component. As used in this disclosure a“pusher component” is a component that pushes and/or thrusts an aircraftthrough a medium. As a non-limiting example, pusher component mayinclude a pusher component such as a pusher propeller, a pusher motor, apusher propulsor, and the like. Additionally, or alternatively, pusherflight component may include a plurality of pusher flight components.

In another embodiment, and still referring to FIG. 3 , propulsor mayinclude a propeller, a blade, or any combination of the two. A propellermay function to convert rotary motion from an engine or other powersource into a swirling slipstream which may push the propeller forwardsor backwards. Propulsor may include a rotating power-driven hub, towhich several radial airfoil-section blades may be attached, such thatan entire whole assembly rotates about a longitudinal axis. As anon-limiting example, blade pitch of propellers may be fixed at a fixedangle, manually variable to a few set positions, automatically variable(e.g. a “constant-speed” type), and/or any combination thereof asdescribed further in this disclosure. As used in this disclosure a“fixed angle” is an angle that is secured and/or substantially unmovablefrom an attachment point. For example, and without limitation, a fixedangle may be an angle of 2.2° inward and/or 1.7° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3.6° outwardand/or 2.7° backward. In an embodiment, propellers for an aircraft maybe designed to be fixed to their hub at an angle similar to the threadon a screw makes an angle to the shaft; this angle may be referred to asa pitch or pitch angle which may determine a speed of forward movementas the blade rotates. Additionally or alternatively, propulsor componentmay be configured having a variable pitch angle. As used in thisdisclosure a “variable pitch angle” is an angle that may be moved and/orrotated. For example, and without limitation, propulsor component may beangled at a first angle of 3.3° inward, wherein propulsor component maybe rotated and/or shifted to a second angle of 1.7° outward.

Still referring to FIG. 3 , propulsor may include a thrust element whichmay be integrated into the propulsor. Thrust element may include,without limitation, a device using moving or rotating foils, such as oneor more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like.

With continued reference to FIG. 3 , plurality of actuators 308 mayinclude power sources, control links to one or more elements, fuses,and/or mechanical couplings used to drive and/or control any otherflight component. Plurality of actuators 308 may include a motor thatoperates to move one or more flight control components and/or one ormore control surfaces, to drive one or more propulsors, or the like. Amotor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof.Alternatively or additionally, a motor may be driven by an inverter. Amotor may also include electronic speed controllers, inverters, or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking.

Still referring to FIG. 3 , plurality of actuators 308 may include anenergy source. An energy source may include, for example, a generator, aphotovoltaic device, a fuel cell such as a hydrogen fuel cell, directmethanol fuel cell, and/or solid oxide fuel cell, an electric energystorage device (e.g. a capacitor, an inductor, and/or a battery). Anenergy source may also include a battery cell, or a plurality of batterycells connected in series into a module and each module connected inseries or in parallel with other modules. Configuration of an energysource containing connected modules may be designed to meet an energy orpower requirement and may be designed to fit within a designatedfootprint in an electric aircraft in which system may be incorporated.

In an embodiment, and still referring to FIG. 3 , an energy source maybe used to provide a steady supply of electrical power to a load over aflight by an electric aircraft 300. For example, energy source may becapable of providing sufficient power for “cruising” and otherrelatively low-energy phases of flight. An energy source may also becapable of providing electrical power for some higher-power phases offlight as well, particularly when the energy source is at a high SOC, asmay be the case for instance during takeoff. In an embodiment, energysource may include an emergency power unit which may be capable ofproviding sufficient electrical power for auxiliary loads includingwithout limitation, lighting, navigation, communications, de-icing,steering, or other systems requiring power or energy. Further, energysource may be capable of providing sufficient power for controlleddescent and landing protocols, including, without limitation, hoveringdescent, or runway landing. As used herein the energy source may havehigh power density where electrical power an energy source can usefullyproduce per unit of volume and/or mass is relatively high. As used inthis disclosure, “electrical power” is a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, forinstance at an expense of maximal total specific energy density or powercapacity. Non-limiting examples of items that may be used as at least anenergy source include batteries used for starting applications includingLi ion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 3 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Modulemay include batteries connected in parallel or in series or a pluralityof modules connected either in series or in parallel designed to satisfyboth power and energy requirements. Connecting batteries in series mayincrease a potential of at least an energy source which may provide morepower on demand. High potential batteries may require cell matching whenhigh peak load is needed. As more cells are connected in strings, theremay exist a possibility of one cell failing which may increaseresistance in module and reduce overall power output as voltage of themodule may decrease as a result of that failing cell. Connectingbatteries in parallel may increase total current capacity by decreasingtotal resistance, and it also may increase overall amp-hour capacity.Overall energy and power outputs of at least an energy source may bebased on individual battery cell performance or an extrapolation basedon a measurement of at least an electrical parameter. In an embodimentwhere energy source includes a plurality of battery cells, overall poweroutput capacity may be dependent on electrical parameters of eachindividual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to a weakest cell. Energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/948,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 3 , according to some embodiments, an energysource may include an emergency power unit (EPU) (i.e., auxiliary powerunit). As used in this disclosure an “emergency power unit” is an energysource as described herein that is configured to power an essentialsystem for a critical function in an emergency, for instance withoutlimitation when another energy source has failed, is depleted, or isotherwise unavailable. Exemplary non-limiting essential systems includenavigation systems, such as MFD, GPS, VOR receiver or directional gyro,and other essential flight components, such as propulsors.

Still referring to FIG. 3 , another exemplary actuator may includelanding gear. Landing gear may be used for take-off and/orlanding/Landing gear may be used to contact ground while aircraft 300 isnot in flight. Exemplary landing gear is disclosed in detail in U.S.patent application Ser. No. 17/196,719 entitled “SYSTEM FOR ROLLINGLANDING GEAR” by R. Griffin et al., which is incorporated in itsentirety herein by reference.

Still referring to FIG. 3 , aircraft 300 may include a pilot control312, including without limitation, a hover control, a thrust control, aninceptor stick, a cyclic, and/or a collective control. As used in thisdisclosure a “collective control” or “collective” is a mechanicalcontrol of an aircraft that allows a pilot to adjust and/or control thepitch angle of the plurality of actuators 308. For example and withoutlimitation, collective control may alter and/or adjust the pitch angleof all of the main rotor blades collectively. For example, and withoutlimitation pilot control 312 may include a yoke control. As used in thisdisclosure a “yoke control” is a mechanical control of an aircraft tocontrol the pitch and/or roll. For example and without limitation, yokecontrol may alter and/or adjust the roll angle of aircraft 300 as afunction of controlling and/or maneuvering ailerons. In an embodiment,pilot control 312 may include one or more footbrakes, control sticks,pedals, throttle levels, and the like thereof. In another embodiment,and without limitation, pilot control 312 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of the aircraft, perpendicular to the wings.For example, and without limitation, a positive yawing motion mayinclude adjusting and/or shifting the nose of aircraft 300 to the right.Principal axis may include a pitch axis. As used in this disclosure a“pitch axis” is an axis that is directed towards the right laterallyextending wing of the aircraft. For example, and without limitation, apositive pitching motion may include adjusting and/or shifting the noseof aircraft 300 upwards. Principal axis may include a roll axis. As usedin this disclosure a “roll axis” is an axis that is directedlongitudinally towards the nose of the aircraft, parallel to thefuselage. For example, and without limitation, a positive rolling motionmay include lifting the left and lowering the right wing concurrently.

Still referring to FIG. 3 , pilot control 312 may be configured tomodify a variable pitch angle. For example, and without limitation,pilot control 312 may adjust one or more angles of attack of apropeller. As used in this disclosure an “angle of attack” is an anglebetween the chord of the propeller and the relative wind. For example,and without limitation angle of attack may include a propeller bladeangled 3.2°. In an embodiment, pilot control 312 may modify the variablepitch angle from a first angle of 2.71° to a second angle of 3.82°.Additionally or alternatively, pilot control 312 may be configured totranslate a pilot desired torque for flight component 308. For example,and without limitation, pilot control 312 may translate that a pilot'sdesired torque for a propeller be 160 lb. ft. of torque. As a furthernon-limiting example, pilot control 312 may introduce a pilot's desiredtorque for a propulsor to be 290 lb. ft. of torque. Additionaldisclosure related to pilot control 312 may be found in U.S. patentapplication Ser. Nos. 17/001,845 and 16/929,206 both of which areentitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT” byC. Spiegel et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 3 , aircraft 300 may include a loading system. Aloading system may include a system configured to load an aircraft ofeither cargo or personnel. For instance, some exemplary loading systemsmay include a swing nose, which is configured to swing the nose ofaircraft 300 of the way thereby allowing direct access to a cargo baylocated behind the nose. A notable exemplary swing nose aircraft isBoeing 747. Additional disclosure related to loading systems can befound in U.S. patent application Ser. No. 17/137,594 entitled “SYSTEMAND METHOD FOR LOADING AND SECURING PAYLOAD IN AN AIRCRAFT” by R.Griffin et al., entirety of which in incorporated herein by reference.

Still referring to FIG. 3 , aircraft 300 may include a sensor 316.Sensor 316 may include any sensor or noise monitoring circuit describedin this disclosure, for instance in reference to FIGS. 1-12 . Sensor 316may be configured to sense a characteristic of pilot control 312. Sensormay be a device, module, and/or subsystem, utilizing any hardware,software, and/or any combination thereof to sense a characteristicand/or changes thereof, in an instant environment, for instance withoutlimitation a pilot control 312, which the sensor is proximal to orotherwise in a sensed communication with, and transmit informationassociated with the characteristic, for instance without limitationdigitized data. Sensor 316 may be mechanically and/or communicativelycoupled to aircraft 300, including, for instance, to at least a pilotcontrol 312. Sensor 316 may be configured to sense a characteristicassociated with at least a pilot control 312. An environmental sensormay include without limitation one or more sensors used to detectambient temperature, barometric pressure, and/or air velocity, one ormore motion sensors which may include without limitation gyroscopes,accelerometers, inertial measurement unit (IMU), and/or magneticsensors, one or more humidity sensors, one or more oxygen sensors, orthe like. Additionally or alternatively, sensor 316 may include at leasta geospatial sensor. Sensor 316 may be located inside an aircraft;and/or be included in and/or attached to at least a portion of theaircraft. Sensor may include one or more proximity sensors, displacementsensors, vibration sensors, and the like thereof. Sensor may be used tomonitor the status of aircraft 300 for both critical and non-criticalfunctions. Sensor may be incorporated into vehicle or aircraft or beremote.

Still referring to FIG. 3 , in some embodiments, sensor 316 may beconfigured to sense a characteristic associated with any pilot controldescribed in this disclosure. Non-limiting examples of a sensor 316 mayinclude an inertial measurement unit (IMU), an accelerometer, agyroscope, a proximity sensor, a pressure sensor, a light sensor, apitot tube, an air speed sensor, a position sensor, a speed sensor, aswitch, a thermometer, a strain gauge, an acoustic sensor, and anelectrical sensor. In some cases, sensor 316 may sense a characteristicas an analog measurement, for instance, yielding a continuously variableelectrical potential indicative of the sensed characteristic. In thesecases, sensor 316 may additionally comprise an analog to digitalconverter (ADC) as well as any additionally circuitry, such as withoutlimitation a Whetstone bridge, an amplifier, a filter, and the like. Forinstance, in some cases, sensor 316 may comprise a strain gageconfigured to determine loading of one or flight components, forinstance landing gear. Strain gage may be included within a circuitcomprising a Whetstone bridge, an amplified, and a bandpass filter toprovide an analog strain measurement signal having a high signal tonoise ratio, which characterizes strain on a landing gear member. An ADCmay then digitize analog signal produces a digital signal that can thenbe transmitted other systems within aircraft 300, for instance withoutlimitation a computing system, a pilot display, and a memory component.Alternatively or additionally, sensor 316 may sense a characteristic ofa pilot control 312 digitally. For instance in some embodiments, sensor316 may sense a characteristic through a digital means or digitize asensed signal natively. In some cases, for example, sensor 316 mayinclude a rotational encoder and be configured to sense a rotationalposition of a pilot control; in this case, the rotational encoderdigitally may sense rotational “clicks” by any known method, such aswithout limitation magnetically, optically, and the like.

Still referring to FIG. 3 , electric aircraft 300 may include at least amotor 1224, which may be mounted on a structural feature of theaircraft. Design of motor 1224 may enable it to be installed external tostructural member (such as a boom, nacelle, or fuselage) for easymaintenance access and to minimize accessibility requirements for thestructure; this may improve structural efficiency by requiring fewerlarge holes in the mounting area. In some embodiments, motor 1224 mayinclude two main holes in top and bottom of mounting area to accessbearing cartridge. Further, a structural feature may include a componentof electric aircraft 300. For example, and without limitation structuralfeature may be any portion of a vehicle incorporating motor 1224,including any vehicle as described in this disclosure. As a furthernon-limiting example, a structural feature may include withoutlimitation a wing, a spar, an outrigger, a fuselage, or any portionthereof; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of many possible features that may function asat least a structural feature. At least a structural feature may beconstructed of any suitable material or combination of materials,including without limitation metal such as aluminum, titanium, steel, orthe like, polymer materials or composites, fiberglass, carbon fiber,wood, or any other suitable material. As a non-limiting example, atleast a structural feature may be constructed from additivelymanufactured polymer material with a carbon fiber exterior; aluminumparts or other elements may be enclosed for structural strength, or forpurposes of supporting, for instance, vibration, torque, or shearstresses imposed by at least propulsor 308. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousmaterials, combinations of materials, and/or constructions techniques.

Still referring to FIG. 3 , electric aircraft 300 may include a verticaltakeoff and landing aircraft (eVTOL). As used herein, a verticaltake-off and landing (eVTOL) aircraft is one that can hover, take off,and land vertically. An eVTOL, as used herein, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft. eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane-stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generated lift and propulsion byway of one or more powered rotors coupled with an engine, such as a“quad copter,” multi-rotor helicopter, or other vehicle that maintainsits lift primarily using downward thrusting propulsors. Fixed-wingflight, as described herein, is where the aircraft is capable of flightusing wings and/or foils that generate life caused by the aircraft'sforward airspeed and the shape of the wings and/or foils, such asairplane-style flight.

With continued reference to FIG. 3 , a number of aerodynamic forces mayact upon the electric aircraft 300 during flight. Forces acting onelectric aircraft 300 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 300 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 300 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 300 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 300 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 300 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 300 downward due to the force of gravity. Anadditional force acting on electric aircraft 300 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor 308 of the electric aircraft.Lift generated by the airfoil may depend on speed of airflow, density ofair, total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 300 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of electric aircraft 300,including without limitation propulsors and/or propulsion assemblies. Inan embodiment, motor 1224 may eliminate need for many externalstructural features that otherwise might be needed to join one componentto another component. Motor 1224 may also increase energy efficiency byenabling a lower physical propulsor profile, reducing drag and/or windresistance. This may also increase durability by lessening the extent towhich drag and/or wind resistance add to forces acting on electricaircraft 300 and/or propulsors.

FIG. 4 illustrates an exemplary embodiment of a battery pack 400 thatmay be housed in the power storage unit to store power. Battery pack 400may be a power storing device that is configured to store electricalenergy in the form of a plurality of battery modules, which themselvesmay be comprised of a plurality of electrochemical cells. These cellsmay utilize electrochemical cells, galvanic cells, electrolytic cells,fuel cells, flow cells, and/or voltaic cells. In general, anelectrochemical cell is a device capable of generating electrical energyfrom chemical reactions or using electrical energy to cause chemicalreactions. Voltaic or galvanic cells are electrochemical cells thatgenerate electric current from chemical reactions, while electrolyticcells generate chemical reactions via electrolysis. In general, the term‘battery’ is used as a collection of cells connected in series orparallel to each other. A battery cell may, when used in conjunctionwith other cells, be electrically connected in series, in parallel or acombination of series and parallel. Series connection comprises wiring afirst terminal of a first cell to a second terminal of a second cell andfurther configured to comprise a single conductive path for electricityto flow while maintaining the same current (measured in Amperes) throughany component in the circuit. A battery cell may use the term ‘wired,’but one of ordinary skill in the art would appreciate that this term issynonymous with ‘electrically connected,’ and that there are many waysto couple electrical elements like battery cells together. An example ofa connector that does not comprise wires may be prefabricated terminalsof a first gender that mate with a second terminal with a second gender.Battery cells may be wired in parallel. Parallel connection compriseswiring a first and second terminal of a first battery cell to a firstand second terminal of a second battery cell and further configured tocomprise more than one conductive path for electricity to flow whilemaintaining the same voltage (measured in Volts) across any component inthe circuit. Battery cells may be wired in a series-parallel circuitwhich combines characteristics of the constituent circuit types to thiscombination circuit. Battery cells may be electrically connected in avirtually unlimited arrangement which may confer onto the system theelectrical advantages associated with that arrangement such ashigh-voltage applications, high current applications, or the like. In anexemplary embodiment, battery pack 400 may include at least 196 batterycells in series and at least 18 battery cells in parallel. This is, assomeone of ordinary skill in the art would appreciate, only an exampleand battery pack 400 may be configured to have a near limitlessarrangement of battery cell configurations.

With continued reference to FIG. 4 , battery pack 400 may include aplurality of battery modules 404. The battery modules may be wiredtogether in series and in parallel. Battery pack 400 may include acenter sheet 408 which may include a thin barrier. The barrier mayinclude a fuse connecting battery modules on either side of center sheet408. The fuse may be disposed in or on center sheet 408 and configuredto connect to an electric circuit comprising a first battery module andtherefore battery unit and cells. In general, and for the purposes ofthis disclosure, a fuse is an electrical safety device that operate toprovide overcurrent protection of an electrical circuit. As asacrificial device, its essential component is metal wire or strip thatmelts when too much current flows through it, thereby interruptingenergy flow. The fuse may comprise a thermal fuse, mechanical fuse,blade fuse, expulsion fuse, spark gap surge arrestor, varistor, or acombination thereof.

Battery pack 400 may also include a side wall 412 which may include alaminate of a plurality of layers configured to thermally insulate theplurality of battery modules 404 from external components of batterypack 400. Side wall 412 layers may include materials which possesscharacteristics suitable for thermal insulation such as fiberglass, air,iron fibers, polystyrene foam, and thin plastic films. Side wall 412 mayadditionally or alternatively electrically insulate the plurality ofbattery modules 404 from external components of battery pack 400 and thelayers of which may include polyvinyl chloride (PVC), glass, asbestos,rigid laminate, varnish, resin, paper, Teflon, rubber, and mechanicallamina. Center sheet 408 may be mechanically coupled to side wall 412.Side wall 412 may include a feature for alignment and coupling to centersheet 408. This feature may comprise a cutout, slots, holes, bosses,ridges, channels, and/or other undisclosed mechanical features, alone orin combination.

Battery pack 400 may also include an end panel 416 having a plurality ofelectrical connectors and further configured to fix battery pack 400 inalignment with at least a side wall 412. End panel 416 may include aplurality of electrical connectors of a first gender configured toelectrically and mechanically couple to electrical connectors of asecond gender. End panel 416 may be configured to convey electricalenergy from battery cells to at least a portion of an eVTOL aircraft.Electrical energy may be configured to power at least a portion of aneVTOL aircraft or comprise signals to notify aircraft computers,personnel, users, pilots, and any others of information regardingbattery health, emergencies, and/or electrical characteristics. Theplurality of electrical connectors may comprise blind mate connectors,plug and socket connectors, screw terminals, ring and spade connectors,blade connectors, and/or an undisclosed type alone or in combination.The electrical connectors of which end panel 416 comprises may beconfigured for power and communication purposes.

A first end of end panel 416 may be configured to mechanically couple toa first end of a first side wall 412 by a snap attachment mechanism,similar to end cap and side panel configuration utilized in the batterymodule. To reiterate, a protrusion disposed in or on end panel 416 maybe captured, at least in part, by a receptacle disposed in or on sidewall 412. A second end of end panel 416 may be mechanically coupled to asecond end of a second side wall 412 in a similar or the same mechanism.

Referring now to FIG. 5 , an embodiment of authentication module 136, aspictured in FIG. 1 , is illustrated in detail. Authentication module 136may include any suitable hardware and/or software module. Authenticationmodule 136 and/or computing device 104 can be configured to authenticateelectric aircraft 108A-D and or any electric aircraft 108A-D of theelectric aircraft fleet. Authenticating, for example and withoutlimitation, can include determining an electric vehicle'sability/authorization to access information included in each moduleand/or engine of the plurality of modules and/or engines operating oncomputing device 104. As a further example and without limitation,authentication may include determining an instructor'sauthorization/ability of access to the information included in eachmodule and/or engine of the plurality of modules and/or enginesoperating on computing device 104. As a further non-limiting example,authentication may include determining an administrator'sauthorization/ability to access the information included in each moduleand/or engine of the plurality of modules and/or engines operating oncomputing device 104. Authentication may enable access to an individualmodule and/or engine, a combination of modules and/or engines, and/orall the modules and/or engines operating on computing device 104. In anon-limiting embodiment, authentication module 136 may be configured toreceive credential 500 from electric aircraft 108A-DA-D. Credential 500may include any credential as described above in further detail inreference to FIG. 1 . For example and without limitation, credential 500may include a username and password unique to the user and/or electricaircraft 108A-D. As a further example and without limitation, credential500 may include a PKI certificate unique to the user and/or electricaircraft 108A-D. As a further embodiment, credential 500 may be receivedfrom remote user device 516 and/or admin device 520, such thatcredential 500 would authenticate an admin device 520, respectively. An“remote user device,” for the purpose of this disclosure, may be a userdevice used by a fleet manager for managing, monitoring, and/orfacilitating communication of the fleet of electric aircraft asdescribed in FIG. 1 . In a non-limiting embodiment, a fleet manager maycommunicate with each electric aircraft of the fleet of electricaircraft 108A-D via remote user device 516. For example and withoutlimitation, the operator may monitor the plurality of electric aircraftsin the sky that are in range and/or connected to the network,authenticate any incoming electric aircraft of the fleet, and facilitatecommunication between the plurality of electric aircrafts which mayinclude transferring a plurality of aircraft data using any means asdescribed herein.

Continuing to refer to FIG. 5 , authentication module 136 and/orcomputing device 104 may be further designed and configured to comparecredential 500 from electric aircraft 108A-D to an authorized credentialstored in authentication database 504. For example, authenticationmodule 136 and/or computing device 104 may be configured to comparecredential 500 from electric aircraft 108A-D to a stored authorizedcredential to determine if credential 500 matches the stored authorizedcredential. As a further embodiment, authentication module 136 and/orcomputing device may compare credential 500 from remote user device 516to an authorized credential stored in authentication database 504. Forexample, authentication module 136 and/or computing device may beconfigured to compare credential 500 from remote user device 516 to astored authorized credential to determine if credential 500 matches thestored authorized credential. As a further non-limiting example,authentication module 136 and/or computing device 112 may matchcredential 500 from admin device 520 to an authorized credential storedin authentication database 504. For example, authentication module 136and/or computing device may be configured to compare credential 500 fromadmin device 520 to a stored authorized credential to determine ifcredential 500 matches the stored authorized credential. In embodiments,comparing credential 500 to an authorized credential stored inauthentication database 504 can include identifying an authorizedcredential stored in authentication database 504 by matching credential500 to at least one authorized credential stored in authenticationdatabase 504. Authentication module 136 and/or computing device 104 mayinclude or communicate with authentication database 504. Authenticationdatabase 504 may be implemented as any database and/or datastoresuitable for use as authentication database 504 as described in theentirety of this disclosure. The “authorized credential” as described inthe entirety of this disclosure, is the unique identifier that willsuccessfully authorize each pilot and/or electric aircraft 108A-DA-D ifreceived. For example and without limitation, the authorized credentialis the correct alpha-numeric spelling, letter case, and specialcharacters of the username and password for electric aircraft 108A-D.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various examples of authorized credentialsthat may be stored in the authentication database consistently with thisdisclosure.

Still referring to FIG. 5 , authentication module 136 and/or computingdevice 104 is further designed and configured to bypass authenticationfor electric aircraft 108A-D based on the identification of theauthorized credential stored within authentication database 504.Bypassing authentication may include permitting access to electricaircraft 108A-D to access the information included in each module and/orengine of the plurality of modules and/or engines operating on computingdevice 104. Bypassing authentication may enable access to an individualmodule and/or engine, a combination of modules and/or engines, and/orall the modules and/or engines operating on computing device 104, asdescribed in further detail in the entirety of this disclosure. As afurther example and without limitation, bypassing authentication mayinclude bypassing authentication for remote user device 516 based on thecomparison of the authorized credential stored in authenticationdatabase 504. As a further non-limiting example, bypassingauthentication may include bypassing authentication for admin device 520based on the comparison of the authorized credential stored inauthentication database 112.

With continued reference to FIG. 5 , authentication module 136 and/orcomputing device 104 may be further configured to authenticate electricaircraft 108A-D as a function of a physical signature authentication. A“physical signature authentication,” for the purpose of this disclosure,is an authentication process that determines an electric vehicle'sability to access the information included in each module and/or engineof the plurality of modules and/or engines operating on computing device104 as a function of a physical signature credential 508. In anon-limiting embodiment, physical signature authentication, in theembodiment, includes receiving physical signature credential 508 fromelectric aircraft 108A-D, comparing and/or matching physical signaturecredential 508 from electric aircraft 108A-D to an authorized physicalsignature credential stored in a physical signature database 512, andbypassing authentication for electric aircraft 108A-D based on thecomparison of the authorized physical signature credential stored withinphysical signature database 512. Physical signature authenticationemploying authentication module 136 may also include authenticatingremote user device 516 and/or admin device 520. Authentication module136 and/or computing device 104 may include or communicate with physicalsignature database 512. Physical signature database 512 may beimplemented as any database and/or datastore suitable for use as aphysical signature database entirely with this disclosure. An exemplaryembodiment of physical signature database 512 is provided below inreference to FIG. 5 . The “physical signature credential” as used inthis disclosure, is any physical identifier, measurement, and/orcalculation utilized for identification purposes regarding an electricvehicle and/or its pilot. In a non-limiting embodiment, physicalsignature credential 508 may include, but not limited to, aphysiological characteristic and/or behavioral characteristic of thepilot associated with the electric vehicle. For example and withoutlimitation, physical signature credential 508 may include vehicle modelnumber, vehicle model type, vehicle battery type, vehicle authoritylevel, pilot authority level, and the like thereof. The “authorizedphysical signature credential” as described in the entirety of thisdisclosure, is unique physical signature identifier that willsuccessfully authorize each user and/or electric aircraft 108A-D, suchthat the authorized physical signature credential is the correctphysical signature credential which will enable the user and/or electricaircraft 108A-D access to the plurality of modules and/or enginesoperating on computing device 104. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousexamples of physical signature credentials and authorized physicalsignature credentials that may be utilized by authentication module 136consistently with this disclosure.

Referring now to FIG. 6 , an embodiment of authentication database 504is illustrated. Authentication database 504 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Authentication database504 may be implemented, without limitation, as a relational database, akey-value retrieval datastore such as a NOSQL database, or any otherformat or structure for use as a datastore that a person skilled in theart would recognize as suitable upon review of the entirety of thisdisclosure. Authorization database 504 may include a plurality of dataentries and/or records corresponding to credentials as described above.Data entries and/or records may describe, without limitation, dataconcerning authorized credential datum and failed credential datum.

With continued reference to FIG. 6 , one or more database tables inauthentication database 504 may include as a non-limiting example anauthorized credential datum table 600. Authorized credential datum table600 may be a table storing authorized credentials, wherein theauthorized credentials may be for electric aircraft 108A-D, remote userdevice, as described in further detail in the entirety of thisdisclosure. For instance, and without limitation, authenticationdatabase 504 may include an authorized credential datum table 600listing unique identifiers stored for electric aircraft 108A-D, whereinthe authorized credential is compared/matched to a credential 500received from electric aircraft 108A-D.

Still referring to FIG. 6 , one or more database tables inauthentication database 504 may include, as a non-limiting example,failed credential datum table 604. A “failed credential,” as describedin the entirety of this disclosure, is a credential received from adevice that did not match an authorized credential stored withinauthorized credential datum table 600 of authentication database 504.Such credentials can be received from electric aircraft 108A-D, remoteuser device 516. Failed credential datum table 604 may be a tablestoring and/or matching failed credentials. For instance and withoutlimitation, authentication database 504 may include failed credentialdatum table 604 listing incorrect unique identifiers received by adevice in authentication module 168, wherein authentication of thedevice did not result. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in authentication database504 consistently with this disclosure.

Referring now to FIG. 7 , an embodiment of physical signature database512 is illustrated. Physical signature database 512 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. Physical signaturedatabase 512 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval datastore such as a NOSQL database, orany other format or structure for use as a datastore that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Physical signature database 512 may includea plurality of data entries and/or records corresponding to elements ofphysical signature datum as described above. Data entries and/or recordsmay describe, without limitation, data concerning particularphysiological characteristics and/or behavioral characteristics thathave been collected. Data entries in a physical signature database 512may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase; one or more additional elements of information may includedata associating a physical signature with one or more cohorts,including demographic groupings such as ethnicity, sex, age, income,geographical region, or the like. Additional elements of information mayinclude one or more categories of physical signature datum as describedabove. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in aphysical signature database 512 may reflect categories, cohorts, and/orpopulations of data consistently with this disclosure.

Still referring to FIG. 7 , one or more database tables in physicalsignature database 512 may include, as a non-limiting example, vehiclemodel data table 700. Vehicle model data table 700 may be a tablecorrelating, relating, and/or matching physical signature credentialsreceived from a device, such as electric aircraft 108A-D and/or remoteuser device 516 as described above, to fingerprint data. For instance,and without limitation, physical signature database 512 may include avehicle model data table 700 listing samples acquired from an electricvehicle having allowed system 100 to retrieve data describing the makeand model of the electric vehicle. The data may be retrieved by anyidentifier scanner that is configured to scan the shape, size, and/orany digital signature incorporated onto the electric vehicle. In anon-limiting embodiment, the electric vehicle itself may transmit themodel data itself. Such data may be inserted in vehicle model data table700.

With continued reference to FIG. 7 , physical signature database 512 mayinclude tables listing one or more samples according to a sample source.As another non-limiting example, physical signature database 512 mayinclude flight plan data table 704, which may list samples acquired froman electric vehicle associated with electric aircraft 108A-D that hasallowed system 100 to obtain information such as a flight plan of theelectric vehicle, destination, cruising speed, and/or the like. Forinstance, and without limitation, physical signature database 512 mayinclude pilot data table 708 listing samples acquired from an electricvehicle by obtaining the information regarding the pilot such as, pilotexperience level, pilot authority level, pilot seniority level, and thelike thereof. As a further non-limiting example, physical signaturedatabase 512 may include a battery system data table 712, which may listsamples acquired from an electric vehicle associated with electricaircraft 108A-D that has allowed system 100 to retrieve the battery packdatum of electric aircraft 108A-D and/or the like. Tables presentedabove are presented for exemplary purposes only; persons skilled in theart will be aware of various ways in which data may be organized inphysical signature database 512 consistently with this disclosure.

Referring now to FIG. 8 , an embodiment of sensor suite 800 ispresented. The herein disclosed system and method may comprise aplurality of sensors in the form of individual sensors or a sensor suiteworking in tandem or individually. In some cases, sensor suite 800 maycommunicate by way of at least a conductor, such as within limitation acontrol signal conductor. Alternatively and/or additionally, in somecases, sensor suite 800 may be communicative by at least a network, forexample any network described in this disclosure including wireless(Wi-Fi), controller area network (CAN), the Internet, and the like. Asensor suite may include a plurality of independent sensors, asdescribed herein, where any number of the described sensors may be usedto detect any number of physical or electrical quantities associatedwith a vehicle battery or an electrical energy storage system, such aswithout limitation charging battery. Independent sensors may includeseparate sensors measuring physical or electrical quantities that may bepowered by and/or in communication with circuits independently, whereeach may signal sensor output to a control circuit such as a usergraphical interface. In a non-limiting example, there may be fourindependent sensors housed in and/or on battery pack measuringtemperature, electrical characteristic such as voltage, amperage,resistance, or impedance, or any other parameters and/or quantities asdescribed in this disclosure. In an embodiment, use of a plurality ofindependent sensors may result in redundancy configured to employ morethan one sensor that measures the same phenomenon, those sensors beingof the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability ofcontroller 104 and/or user to detect phenomenon is maintained.

With continued reference to FIG. 8 , sensor suite 800 may include ahumidity sensor 804. Humidity, as used in this disclosure, is theproperty of a gaseous medium (almost always air) to hold water in theform of vapor. An amount of water vapor contained within a parcel of aircan vary significantly. Water vapor is generally invisible to the humaneye and may be damaging to electrical components. There are threeprimary measurements of humidity, absolute, relative, specific humidity.“Absolute humidity,” for the purposes of this disclosure, describes thewater content of air and is expressed in either grams per cubic metersor grams per kilogram. “Relative humidity,” for the purposes of thisdisclosure, is expressed as a percentage, indicating a present stat ofabsolute humidity relative to a maximum humidity given the sametemperature. “Specific humidity,” for the purposes of this disclosure,is the ratio of water vapor mass to total moist air parcel mass, whereparcel is a given portion of a gaseous medium. Humidity sensor 804 maybe psychrometer. Humidity sensor 804 may be a hygrometer. Humiditysensor 804 may be configured to act as or include a humidistat. A“humidistat,” for the purposes of this disclosure, is ahumidity-triggered switch, often used to control another electronicdevice. Humidity sensor 804 may use capacitance to measure relativehumidity and include in itself, or as an external component, include adevice to convert relative humidity measurements to absolute humiditymeasurements. “Capacitance,” for the purposes of this disclosure, is theability of a system to store an electric charge, in this case the systemis a parcel of air which may be near, adjacent to, or above a batterycell.

With continued reference to FIG. 8 , sensor suite 800 may includemultimeter 808. Multimeter 808 may be configured to measure voltageacross a component, electrical current through a component, andresistance of a component. Multimeter 808 may include separate sensorsto measure each of the previously disclosed electrical characteristicssuch as voltmeter, ammeter, and ohmmeter, respectively. Alternatively oradditionally, and with continued reference to FIG. 8 , sensor suite 800may include a sensor or plurality thereof that may detect voltage anddirect charging of individual battery cells according to charge level;detection may be performed using any suitable component, set ofcomponents, and/or mechanism for direct or indirect measurement and/ordetection of voltage levels, including without limitation comparators,analog to digital converters, any form of voltmeter, or the like. Sensorsuite 800 and/or a control circuit incorporated therein and/orcommunicatively connected thereto may be configured to adjust charge toone or more battery cells as a function of a charge level and/or adetected parameter. For instance, and without limitation, sensor suite800 may be configured to determine that a charge level of a battery cellis high based on a detected voltage level of that battery cell orportion of the battery pack. Sensor suite 800 may alternatively oradditionally detect a charge reduction event, defined for purposes ofthis disclosure as any temporary or permanent state of a battery cellrequiring reduction or cessation of charging; a charge reduction eventmay include a cell being fully charged and/or a cell undergoing aphysical and/or electrical process that makes continued charging at acurrent voltage and/or current level inadvisable due to a risk that thecell will be damaged, will overheat, or the like. Detection of a chargereduction event may include detection of a temperature, of the cellabove a threshold level, detection of a voltage and/or resistance levelabove or below a threshold, or the like. Sensor suite 800 may includedigital sensors, analog sensors, or a combination thereof. Sensor suite800 may include digital-to-analog converters (DAC), analog-to-digitalconverters (ADC, A/D, A-to-D), a combination thereof, or other signalconditioning components used in transmission of a battery sensor signalto a destination over wireless or wired connection.

With continued reference to FIG. 8 , sensor suite 800 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensorsuite 800, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin(° K), or another scale alone or in combination. The temperaturemeasured by sensors may comprise electrical signals which aretransmitted to their appropriate destination wireless or through a wiredconnection.

With continued reference to FIG. 8 , sensor suite 800 may include asensor configured to detect gas that may be emitted during or after acatastrophic cell failure. “Catastrophic cell failure,” for the purposesof this disclosure, refers to a malfunction of a battery cell, which maybe an electrochemical cell, which renders the cell inoperable for itsdesigned function, namely providing electrical energy to at least aportion of an electric aircraft. Byproducts of catastrophic cell failure812 may include gaseous discharge including oxygen, hydrogen, carbondioxide, methane, carbon monoxide, a combination thereof, or anotherundisclosed gas, alone or in combination. Further the sensor configuredto detect vent gas from electrochemical cells may comprise a gasdetector. For the purposes of this disclosure, a “gas detector” is adevice used to detect a gas is present in an area. Gas detectors, andmore specifically, the gas sensor that may be used in sensor suite 800,may be configured to detect combustible, flammable, toxic, oxygendepleted, a combination thereof, or another type of gas alone or incombination. The gas sensor that may be present in sensor suite 800 mayinclude a combustible gas, photoionization detectors, electrochemicalgas sensors, ultrasonic sensors, metal-oxide-semiconductor (MOS)sensors, infrared imaging sensors, a combination thereof, or anotherundisclosed type of gas sensor alone or in combination. Sensor suite 800may include sensors that are configured to detect non-gaseous byproductsof catastrophic cell failure 812 including, in non-limiting examples,liquid chemical leaks including aqueous alkaline solution, ionomer,molten phosphoric acid, liquid electrolytes with redox shuttle andionomer, and salt water, among others. Sensor suite 800 may includesensors that are configured to detect non-gaseous byproducts ofcatastrophic cell failure 812 including, in non-limiting examples,electrical anomalies as detected by any of the previous disclosedsensors or components.

With continued reference to FIG. 8 , sensor suite 800 may be configuredto detect events where voltage nears an upper voltage threshold or lowervoltage threshold. The upper voltage threshold may be stored in datastorage system for comparison with an instant measurement taken by anycombination of sensors present within sensor suite 800. The uppervoltage threshold may be calculated and calibrated based on factorsrelating to battery cell health, maintenance history, location withinbattery pack, designed application, and type, among others. Sensor suite800 may measure voltage at an instant, over a period of time, orperiodically. Sensor suite 800 may be configured to operate at any ofthese detection modes, switch between modes, or simultaneous measure inmore than one mode. Controller 104 may detect through sensor suite 800events where voltage nears the lower voltage threshold. The lowervoltage threshold may indicate power loss to or from an individualbattery cell or portion of the battery pack. Controller 104 may detectthrough sensor suite 800 events where voltage exceeds the upper andlower voltage threshold. Events where voltage exceeds the upper andlower voltage threshold may indicate battery cell failure or electricalanomalies that could lead to potentially dangerous situations foraircraft and personnel that may be present in or near its operation.

With continued reference to FIG. 8 , in some cases, sensor suite 800 mayinclude a swell sensor configured to sense swell, pressure, or strain ofat least a battery cell. In some cases, battery cell swell, pressure,and/or strain may be indicative of an amount of gases and/or gasexpansion within a battery cell. Battery swell sensor may include one ormore of a pressure sensor, a load cell, and a strain gauge. In somecases, battery swell sensor may output a battery swell signal that isanalog and requires signal processing techniques. For example, in somecases, wherein battery swell sensor includes at least a strain gauge,battery swell signal may be processed and digitized by one or more of aWheatstone bridge, an amplifier, a filter, and an analog to digitalconverter. In some cases, battery sensor signal may include batteryswell signal.

Now referring to FIG. 9 , an exemplary embodiment 900 of a flightcontroller 904 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 904 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 904may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 904 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 9 , flight controller 904may include a signal transformation component 908. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 908 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component908 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 908 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 908 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 908 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 9 , signal transformation component 908 may beconfigured to optimize an intermediate representation 912. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 908 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 908 may optimizeintermediate representation 912 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 908 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 908 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 904. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 908 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 9 , flight controller 904may include a reconfigurable hardware platform 916. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 916 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 9 , reconfigurable hardware platform 916 mayinclude a logic component 920. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 920 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 920 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 920 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 920 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 920 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 912. Logiccomponent 920 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 904. Logiccomponent 920 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 920 may beconfigured to execute the instruction on intermediate representation 912and/or output language. For example, and without limitation, logiccomponent 920 may be configured to execute an addition operation onintermediate representation 912 and/or output language.

In an embodiment, and without limitation, logic component 920 may beconfigured to calculate a flight element 924. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 924 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 924 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 924 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 9 , flight controller 904 may include a chipsetcomponent 928. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 928 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 920 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 928 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 920 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 928 maymanage data flow between logic component 920, memory cache, and a flightcomponent 208. As used in this disclosure (and with particular referenceto FIG. 9 ) a “flight component” is a portion of an aircraft that can bemoved or adjusted to affect one or more flight elements. For example,flight component 208 may include a component used to affect theaircrafts' roll and pitch which may comprise one or more ailerons. As afurther example, flight component 208 may include a rudder to controlyaw of an aircraft. In an embodiment, chipset component 928 may beconfigured to communicate with a plurality of flight components as afunction of flight element 924. For example, and without limitation,chipset component 928 may transmit to an aircraft rotor to reduce torqueof a first lift propulsor and increase the forward thrust produced by apusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 9 , flight controller 904may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 904 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 924. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 904 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 904 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 9 , flight controller 904may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 924 and a pilot signal936 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 936may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 936 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 936may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 936 may include an explicitsignal directing flight controller 904 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 936 may include an implicit signal, wherein flight controller 904detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 936 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 936 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 936 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 936 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal936 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 9 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 904 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 904.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 9 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 904 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 9 , flight controller 904 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor, and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 904. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 904 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 904 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 9 , flight controller 904 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus, or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 9 , flight controller 904may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller904 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 904 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 904 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 9 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 208. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 9 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 904. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 912 and/or output language from logiccomponent 920, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 9 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 9 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 9 , flight controller 904 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 904 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 9 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 9 , flight controller may include asub-controller 940. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 904 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 940may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 940 may include any component of any flightcontroller as described above. Sub-controller 940 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 940may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 940 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 9 , flight controller may include aco-controller 944. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 904 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 944 mayinclude one or more controllers and/or components that are similar toflight controller 904. As a further non-limiting example, co-controller944 may include any controller and/or component that joins flightcontroller 904 to distributer flight controller. As a furthernon-limiting example, co-controller 944 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 904 to distributed flight control system. Co-controller 944may include any component of any flight controller as described above.Co-controller 944 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 9 , flightcontroller 904 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 904 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring to FIG. 10 , an avionic mesh network 1000 is schematicallyillustrated. According to some embodiments, an avionic mesh network mayinclude a single network.

Alternatively or additionally, an avionic mesh network may include morethan a single network. A single networks may be differentiated accordingto address, for example Internet Protocol address, gateway, or nameserver used. For example, in some cases, multiple networks may usedifferent gateways, even though the multiple networks may still bewithin communicative connection with one another.

With continued reference to FIG. 10 , in some embodiments, an avionicmesh network 1000 may include inter-aircraft network nodes,intra-aircraft network nodes, as well as non-aircraft network nodes. Asused in this disclosure, a “network node” is any componentcommunicatively coupled to at least a network. For example, a networknode may include an endpoint, for example a computing device on network,a switch, a router, a bridge, and the like. A network node may include aredistribution point, for example a switch, or an endpoint, for examplea component communicatively connected to network. As used in thisdisclosure, “inter-aircraft network nodes” are two or more network nodesthat are physically located in two or more aircraft and communicativelyconnected. As used in this disclosure, “intra-aircraft network nodes”are two or more network nodes that are each physically located within asingle aircraft and communicatively connected. As used in thisdisclosure, a “non-aircraft network node” is a network node that is notlocated on an aircraft and is communicatively connected to a network.

With continued reference to FIG. 10 , in some embodiments, avionic meshnetwork 1000 may include a wireless mesh network organized in a meshtopology. A mesh topology may include a networked infrastructure inwhich network nodes may be connected directly, dynamically, and/ornon-hierarchically to many other nodes (e.g., as many other nodes aspossible). In some cases, a mesh topology may facilitate cooperationbetween network nodes, for example redistributive network nodes, inrouting of communication between network participants (e.g., othernetwork nodes). A mesh topology may facilitate a lack of dependency onany given node, thereby allowing other nodes to participate in relayingcommunication. In some cases, mesh networks may dynamicallyself-organize and self-configure. Self-configuration enables dynamicdistribution of workloads, particularly in event a network node failure,thereby contributing to fault-tolerance and reduced maintenancerequirements. In some embodiments, mesh networks can relay messagesusing either a flooding technique or a routing technique. A floodingtechnique sends a message to every network node, flooding network withthe message. A routing technique allows a mesh network to communicate amessage is propagated along a determined nodal path to the message'sintended destination. Message routing may be performed by mesh networksin part by ensuring that all nodal paths are available. Nodal pathavailability may be ensured by maintaining continuous nodal networkconnections and reconfiguring nodal paths with an occurrence of brokennodal paths. Reconfiguration of nodal paths, in some cases, may beperformed by utilizing self-healing algorithms, such as withoutlimitation Shortest Path Bridging. Self-healing allows a routing-basednetwork to operate when a node fails or when a connection becomesunreliable. In some embodiments, a mesh network having all network nodesconnected to each other may be termed a fully connected network. Fullyconnected wired networks have advantages of security and reliability.For example, an unreliable wired connection between two wired networknodes will only affect only two nodes attached to the unreliable wiredconnection.

With continued reference to FIG. 10 , an exemplary avionic mesh network1000 is shown providing communicative connection between a computingdevice 1004 and aircraft 1008A-C. Computing device 1004 may include anycomputing device described in this disclosure. In some embodiments,computing device 1004 may be connected to a terrestrial network 1012.Terrestrial networks 1012 may include any network described in thisdisclosure and may include, without limitation, wireless networks, localarea networks (LANs), wide area networks (WANs), ethernet, Internet,mobile broadband, fiber optic communication, and the like. In somecases, a grounded aircraft 1008C may be connected to an avionic meshnetwork 1000 by way of a terrestrial network 1012. In some cases,avionic mesh network 1000 may include a wireless communication node1016. A wireless communication node 1016 may provide communicativeconnection by way of wireless networking. Wireless networking mayinclude any wireless network method described in this disclosure,including without limitation Wi-Fi, mobile broadband, opticalcommunication, radio communication, and the like. In some cases,wireless communication node 1016 may be configured to connect with afirst airborne aircraft in flight 1008A. First airborne aircraft in someembodiments may include at least a first intra-aircraft network node1020A. As described above, first intra-aircraft network node 1020A maybe configured to connect to other nodes within first airborne aircraft1008A. In some cases, avionic mesh network 1000 may be configured toprovide inter-aircraft communication, for instance by using a firstinter-aircraft network node 1024A. In some cases, first inter-aircraftnetwork node may be configured to communicate with a secondinter-aircraft network node 1024B. Inter-aircraft nodes 1020A-B mayinclude radio communication and/or optical wireless communication, forexample free space optical communication.

With continued reference to FIG. 10 , avionic mesh network 1000 may beadditionally configured to provide for encrypted and/or securedcommunication between components, i.e., nodes, communicative on thenetwork. In some cases, encrypted communication on network 1000 may beprovided for by way of end-to-end encryption. Exemplary non-limitedend-to-end encryption methods include symmetric key encryption,asymmetric key encryption, public key encryption methods, private keyencryption methods and the like. In some cases, avionic mesh network1000 and/or another network may be configured to provide secure keyexchange for encryption methods. Exemplary non-limiting key exchangemethods include Diffie-Hellman key exchange, Supersingular isogeny keyexchange, use of at least a trusted key authority, passwordauthenticated key agreement, forward secrecy, quantum key exchange, andthe like. In some cases, an avionic mesh network 1000 may include atleast an optical network component, for example fiber optic cables,wireless optical networks, and/or free space optical network. In somecases, encrypted communication between network nodes may be implementedby way of optical network components. For example, quantum key exchangein some embodiments, may defeat man-in-the-middle attacks. This isgenerally because, observation of a quantum system disturbs the quantumsystem. Quantum key exchange in some cases, uses this generalcharacteristic of quantum physics to communicate sensitive information,such as an encryption key, by encoding the sensitive information inpolarization state of quantum of radiation. At least a polarizationsensitive detector may be used to decode sensitive information.

Still referring to FIG. 10 , in an embodiment, methods and systemsdescribed herein may perform or implement one or more aspects of acryptographic system. In one embodiment, a cryptographic system is asystem that converts data from a first form, known as “plaintext,” whichis intelligible when viewed in its intended format, into a second form,known as “ciphertext,” which is not intelligible when viewed in the sameway. Ciphertext may be unintelligible in any format unless firstconverted back to plaintext. In one embodiment, a process of convertingplaintext into ciphertext is known as “encryption.” Encryption processmay involve the use of a datum, known as an “encryption key,” to alterplaintext. Cryptographic system may also convert ciphertext back intoplaintext, which is a process known as “decryption.” Decryption processmay involve the use of a datum, known as a “decryption key,” to returnthe ciphertext to its original plaintext form. In embodiments ofcryptographic systems that are “symmetric,” decryption key isessentially the same as encryption key: possession of either key makesit possible to deduce the other key quickly without further secretknowledge. Encryption and decryption keys in symmetric cryptographicsystems may be kept secret and shared only with persons or entities thatthe user of the cryptographic system wishes to be able to decrypt theciphertext. One example of a symmetric cryptographic system is theAdvanced Encryption Standard (“AES”), which arranges plaintext intomatrices and then modifies the matrices through repeated permutationsand arithmetic operations with an encryption key.

Still referring to FIG. 10 , in embodiments of cryptographic systemsthat are “asymmetric,” either encryption or decryption key cannot bereadily deduced without additional secret knowledge, even given thepossession of a corresponding decryption or encryption key,respectively; a common example is a “public key cryptographic system,”in which possession of the encryption key does not make it practicallyfeasible to deduce the decryption key, so that the encryption key maysafely be made available to the public. An example of a public keycryptographic system is RSA, in which an encryption key involves the useof numbers that are products of very large prime numbers, but adecryption key involves the use of those very large prime numbers, suchthat deducing the decryption key from the encryption key requires thepractically infeasible task of computing the prime factors of a numberwhich is the product of two very large prime numbers. Another example iselliptic curve cryptography, which relies on the fact that given twopoints P and Q on an elliptic curve over a finite field, and adefinition for addition where A+B=−R, the point where a line connectingpoint A and point B intersects the elliptic curve, where “0,” theidentity, is a point at infinity in a projective plane containing theelliptic curve, finding a number k such that adding P to itself k timesresults in Q is computationally impractical, given correctly selectedelliptic curve, finite field, and P and Q.

With continued reference to FIG. 10 , in some cases, avionic meshnetwork 1000 may be configured to allow message authentication betweennetwork nodes. In some cases, message authentication may include aproperty that a message has not been modified while in transit and thatreceiving party can verify source of the message. In some embodiments,message authentication may include us of message authentication codes(MACs), authenticated encryption (AE), and/or digital signature. Messageauthentication code, also known as digital authenticator, may be used asan integrity check based on a secret key shared by two parties toauthenticate information transmitted between them. In some cases, adigital authenticator may use a cryptographic hash and/or an encryptionalgorithm.

Still referring to FIG. 10 , in some embodiments, systems and methodsdescribed herein produce cryptographic hashes, also referred to by theequivalent shorthand term “hashes.” A cryptographic hash, as usedherein, is a mathematical representation of a lot of data, such as filesor blocks in a block chain as described in further detail below; themathematical representation is produced by a lossy “one-way” algorithmknown as a “hashing algorithm.” Hashing algorithm may be a repeatableprocess; that is, identical lots of data may produce identical hasheseach time they are subjected to a particular hashing algorithm. Becausehashing algorithm is a one-way function, it may be impossible toreconstruct a lot of data from a hash produced from the lot of datausing the hashing algorithm. In the case of some hashing algorithms,reconstructing the full lot of data from the corresponding hash using apartial set of data from the full lot of data may be possible only byrepeatedly guessing at the remaining data and repeating the hashingalgorithm; it is thus computationally difficult if not infeasible for asingle computer to produce the lot of data, as the statisticallikelihood of correctly guessing the missing data may be extremely low.However, the statistical likelihood of a computer of a set of computerssimultaneously attempting to guess the missing data within a usefultimeframe may be higher, permitting mining protocols as described infurther detail below.

Still referring to FIG. 10 , in an embodiment, hashing algorithm maydemonstrate an “avalanche effect,” whereby even extremely small changesto lot of data produce drastically different hashes. This may thwartattempts to avoid the computational work necessary to recreate a hash bysimply inserting a fraudulent datum in data lot, enabling the use ofhashing algorithms for “tamper-proofing” data such as data contained inan immutable ledger as described in further detail below. This avalancheor “cascade” effect may be evinced by various hashing processes; personsskilled in the art, upon reading the entirety of this disclosure, willbe aware of various suitable hashing algorithms for purposes describedherein. Verification of a hash corresponding to a lot of data may beperformed by running the lot of data through a hashing algorithm used toproduce the hash. Such verification may be computationally expensive,albeit feasible, potentially adding up to significant processing delayswhere repeated hashing, or hashing of large quantities of data, isrequired, for instance as described in further detail below. Examples ofhashing programs include, without limitation, SHA256, a NIST standard;further current and past hashing algorithms include Winternitz hashingalgorithms, various generations of Secure Hash Algorithm (including“SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as“MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny(e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), MessageAuthentication Code (“MAC”)-family hash functions such as PMAC, OMAC,VMAC, HMAC, and UMAC, Poly1305-AES, Elliptic Curve Only Hash (“ECOH”)and similar hash functions, Fast-Syndrome-based (FSB) hash functions,GOST hash functions, the Grøstl hash function, the HAS-160 hashfunction, the JH hash function, the RadioGatún hash function, the Skeinhash function, the Streebog hash function, the SWIFFT hash function, theTiger hash function, the Whirlpool hash function, or any hash functionthat satisfies, at the time of implementation, the requirements that acryptographic hash be deterministic, infeasible to reverse-hash,infeasible to find collisions, and have the property that small changesto an original message to be hashed will change the resulting hash soextensively that the original hash and the new hash appear uncorrelatedto each other. A degree of security of a hash function in practice maydepend both on the hash function itself and on characteristics of themessage and/or digest used in the hash function. For example, where amessage is random, for a hash function that fulfillscollision-resistance requirements, a brute-force or “birthday attack”may to detect collision may be on the order of O(2n/2) for n outputbits; thus, it may take on the order of 2256 operations to locate acollision in a 512 bit output “Dictionary” attacks on hashes likely tohave been generated from a non-random original text can have a lowercomputational complexity, because the space of entries they are guessingis far smaller than the space containing all random permutations ofbits. However, the space of possible messages may be augmented byincreasing the length or potential length of a possible message, or byimplementing a protocol whereby one or more randomly selected strings orsets of data are added to the message, rendering a dictionary attacksignificantly less effective.

Continuing to refer to FIG. 10 , a “secure proof,” as used in thisdisclosure, is a protocol whereby an output is generated thatdemonstrates possession of a secret, such as device-specific secret,without demonstrating the entirety of the device-specific secret; inother words, a secure proof by itself, is insufficient to reconstructthe entire device-specific secret, enabling the production of at leastanother secure proof using at least a device-specific secret. A secureproof may be referred to as a “proof of possession” or “proof ofknowledge” of a secret. Where at least a device-specific secret is aplurality of secrets, such as a plurality of challenge-response pairs, asecure proof may include an output that reveals the entirety of one ofthe plurality of secrets, but not all of the plurality of secrets; forinstance, secure proof may be a response contained in onechallenge-response pair. In an embodiment, proof may not be secure; inother words, proof may include a one-time revelation of at least adevice-specific secret, for instance as used in a singlechallenge-response exchange.

Still referring to FIG. 10 , secure proof may include a zero-knowledgeproof, which may provide an output demonstrating possession of a secretwhile revealing none of the secret to a recipient of the output;zero-knowledge proof may be information-theoretically secure, meaningthat an entity with infinite computing power would be unable todetermine secret from output. Alternatively, zero-knowledge proof may becomputationally secure, meaning that determination of secret from outputis computationally infeasible, for instance to the same extent thatdetermination of a private key from a public key in a public keycryptographic system is computationally infeasible. Zero-knowledge proofalgorithms may generally include a set of two algorithms, a proveralgorithm, or “P,” which is used to prove computational integrity and/orpossession of a secret, and a verifier algorithm, or “V” whereby a partymay check the validity of P. Zero-knowledge proof may include aninteractive zero-knowledge proof, wherein a party verifying the proofmust directly interact with the proving party; for instance, theverifying and proving parties may be required to be online, or connectedto the same network as each other, at the same time. Interactivezero-knowledge proof may include a “proof of knowledge” proof, such as aSchnorr algorithm for proof on knowledge of a discrete logarithm. in aSchnorr algorithm, a prover commits to a randomness r, generates amessage based on r, and generates a message adding r to a challenge cmultiplied by a discrete logarithm that the prover is able to calculate;verification is performed by the verifier who produced c byexponentiation, thus checking the validity of the discrete logarithm.Interactive zero-knowledge proofs may alternatively or additionallyinclude sigma protocols. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various alternativeinteractive zero-knowledge proofs that may be implemented consistentlywith this disclosure.

Still referring to FIG. 10 , alternatively, zero-knowledge proof mayinclude a non-interactive zero-knowledge, proof, or a proof whereinneither party to the proof interacts with the other party to the proof;for instance, each of a party receiving the proof and a party providingthe proof may receive a reference datum which the party providing theproof may modify or otherwise use to perform the proof. As anon-limiting example, zero-knowledge proof may include a succinctnon-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a“trusted setup” process creates proof and verification keys using secret(and subsequently discarded) information encoded using a public keycryptographic system, a prover runs a proving algorithm using theproving key and secret information available to the prover, and averifier checks the proof using the verification key; public keycryptographic system may include RSA, elliptic curve cryptography,ElGamal, or any other suitable public key cryptographic system.Generation of trusted setup may be performed using a secure multipartycomputation so that no one party has control of the totality of thesecret information used in the trusted setup; as a result, if any oneparty generating the trusted setup is trustworthy, the secretinformation may be unrecoverable by malicious parties. As anothernon-limiting example, non-interactive zero-knowledge proof may include aSuccinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledgeproof. In an embodiment, a ZK-STARKS proof includes a Merkle root of aMerkle tree representing evaluation of a secret computation at somenumber of points, which may be 1 billion points, plus Merkle branchesrepresenting evaluations at a set of randomly selected points of thenumber of points; verification may include determining that Merklebranches provided match the Merkle root, and that point verifications atthose branches represent valid values, where validity is shown bydemonstrating that all values belong to the same polynomial created bytransforming the secret computation. In an embodiment, ZK-STARKS doesnot require a trusted setup.

Still referring to FIG. 10 , zero-knowledge proof may include any othersuitable zero-knowledge proof. Zero-knowledge proof may include, withoutlimitation bulletproofs. Zero-knowledge proof may include a homomorphicpublic-key cryptography (hPKC)-based proof. Zero-knowledge proof mayinclude a discrete logarithmic problem (DLP) proof. Zero-knowledge proofmay include a secure multi-party computation (MPC) proof. Zero-knowledgeproof may include, without limitation, an incrementally verifiablecomputation (IVC). Zero-knowledge proof may include an interactiveoracle proof (IOP). Zero-knowledge proof may include a proof based onthe probabilistically checkable proof (PCP) theorem, including a linearPCP (LPCP) proof. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms ofzero-knowledge proofs that may be used, singly or in combination,consistently with this disclosure.

Still referring to FIG. 10 , in an embodiment, secure proof isimplemented using a challenge-response protocol. In an embodiment, thismay function as a one-time pad implementation; for instance, amanufacturer or other trusted party may record a series of outputs(“responses”) produced by a device possessing secret information, givena series of corresponding inputs (“challenges”), and store themsecurely. In an embodiment, a challenge-response protocol may becombined with key generation. A single key may be used in one or moredigital signatures as described in further detail below, such assignatures used to receive and/or transfer possession of crypto-currencyassets; the key may be discarded for future use after a set period oftime. In an embodiment, varied inputs include variations in localphysical parameters, such as fluctuations in local electromagneticfields, radiation, temperature, and the like, such that an almostlimitless variety of private keys may be so generated. Secure proof mayinclude encryption of a challenge to produce the response, indicatingpossession of a secret key. Encryption may be performed using a privatekey of a public key cryptographic system, or using a private key of asymmetric cryptographic system; for instance, trusted party may verifyresponse by decrypting an encryption of challenge or of another datumusing either a symmetric or public-key cryptographic system, verifyingthat a stored key matches the key used for encryption as a function ofat least a device-specific secret. Keys may be generated by randomvariation in selection of prime numbers, for instance for the purposesof a cryptographic system such as RSA that relies prime factoringdifficulty. Keys may be generated by randomized selection of parametersfor a seed in a cryptographic system, such as elliptic curvecryptography, which is generated from a seed. Keys may be used togenerate exponents for a cryptographic system such as Diffie-Helman orElGamal that are based on the discrete logarithm problem.

Still referring to FIG. 10 , as described above in some embodiments anavionic mesh network 1000 may provide secure and/or encryptedcommunication at least in part by employing digital signatures. A“digital signature,” as used herein, includes a secure proof ofpossession of a secret by a signing device, as performed on providedelement of data, known as a “message.” A message may include anencrypted mathematical representation of a file or other set of datausing the private key of a public key cryptographic system. Secure proofmay include any form of secure proof as described above, includingwithout limitation encryption using a private key of a public keycryptographic system as described above. Signature may be verified usinga verification datum suitable for verification of a secure proof; forinstance, where secure proof is enacted by encrypting message using aprivate key of a public key cryptographic system, verification mayinclude decrypting the encrypted message using the corresponding publickey and comparing the decrypted representation to a purported match thatwas not encrypted; if the signature protocol is well-designed andimplemented correctly, this means the ability to create the digitalsignature is equivalent to possession of the private decryption keyand/or device-specific secret. Likewise, if a message making up amathematical representation of file is well-designed and implementedcorrectly, any alteration of the file may result in a mismatch with thedigital signature; the mathematical representation may be produced usingan alteration-sensitive, reliably reproducible algorithm, such as ahashing algorithm as described above. A mathematical representation towhich the signature may be compared may be included with signature, forverification purposes; in other embodiments, the algorithm used toproduce the mathematical representation may be publicly available,permitting the easy reproduction of the mathematical representationcorresponding to any file.

Still viewing FIG. 10 , in some embodiments, digital signatures may becombined with or incorporated in digital certificates. In oneembodiment, a digital certificate is a file that conveys information andlinks the conveyed information to a “certificate authority” that is theissuer of a public key in a public key cryptographic system. Certificateauthority in some embodiments contains data conveying the certificateauthority's authorization for the recipient to perform a task. Theauthorization may be the authorization to access a given datum. Theauthorization may be the authorization to access a given process. Insome embodiments, the certificate may identify the certificateauthority. The digital certificate may include a digital signature.

With continued reference to FIG. 10 , in some embodiments, a third partysuch as a certificate authority (CA) is available to verify that thepossessor of the private key is a particular entity; thus, if thecertificate authority may be trusted, and the private key has not beenstolen, the ability of an entity to produce a digital signature confirmsthe identity of the entity and links the file to the entity in averifiable way. Digital signature may be incorporated in a digitalcertificate, which is a document authenticating the entity possessingthe private key by authority of the issuing certificate authority andsigned with a digital signature created with that private key and amathematical representation of the remainder of the certificate. Inother embodiments, digital signature is verified by comparing thedigital signature to one known to have been created by the entity thatpurportedly signed the digital signature; for instance, if the publickey that decrypts the known signature also decrypts the digitalsignature, the digital signature may be considered verified. Digitalsignature may also be used to verify that the file has not been alteredsince the formation of the digital signature.

Referring now to FIG. 11 , an exemplary method 1100 for of use forencrypting external communications for an electric aircraft. An electricaircraft may include any electric vehicle described in this disclosure,for example with reference to FIGS. 1-11 . At step 1105, method 1100 mayinclude communicating, using a communication module configured tocommunicate with a network node. A communication module may include anycommunication module described in this disclosure, for example withreference to FIGS. 1-11 . A network node may include any node describedin this disclosure, for example with reference to FIGS. 1-11 .

Referring now to FIG. 11 , At step 1110, method 1100 may includepowering, using a battery pack configured to power the electricaircraft. A battery pack may include any battery described in thisdisclosure, for example with reference to FIGS. 1-11 .

Referring now to FIG. 11 , At step 1115, method 1100 may includesensing, using a battery sensor, configured to generate battery datum. Abattery sensor may include any sensor described in this disclosure, forexample with reference to FIGS. 1-11 . A battery datum may include anydatum described in this disclosure, for example with reference to FIGS.1-11 .

Referring now to FIG. 11 , At step 1120, method 1100 may includecomputing, using a computing device that is communicatively connected tothe communication module and the battery sensor. A computing device mayinclude any computing device described in this disclosure, for examplewith reference to FIGS. 1-11 .

Referring now to FIG. 11 , At step 1125, method 1100 may includereceiving, using a computing device the battery datum.

Referring now to FIG. 11 , At step 1130, method 1100 may includeencrypting, using the computing device the battery datum using anencryption process. Encryption process may include any encryptionprocess described in this disclosure, for example with reference toFIGS. 1-11 .

Referring now to FIG. 11 , At step 1135, method 1100 may includeidentifying using a computing device the network node.

Referring now to FIG. 11 , At step 1140, method 1100 may includetransmitting, the encrypted battery datum to a network node using acommunication module.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 12 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1200 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1200 includes a processor 1204 and a memory1208 that communicate with each other, and with other components, via abus 1212. Bus 1212 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1204 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1204 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1204 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1208 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1216 (BIOS), including basic routines thathelp to transfer information between elements within computer system1200, such as during start-up, may be stored in memory 1208. Memory 1208may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1220 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1208 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1200 may also include a storage device 1224. Examples ofa storage device (e.g., storage device 1224) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1224 may beconnected to bus 1212 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1224 (or one or more components thereof) may be removably interfacedwith computer system 1200 (e.g., via an external port connector (notshown)). Particularly, storage device 1224 and an associatedmachine-readable medium 1228 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1200. In one example,software 1220 may reside, completely or partially, withinmachine-readable medium 1228. In another example, software 1220 mayreside, completely or partially, within processor 1204.

Computer system 1200 may also include an input device 1232. In oneexample, a user of computer system 1200 may enter commands and/or otherinformation into computer system 1200 via input device 1232. Examples ofan input device 1232 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1232may be interfaced to bus 1212 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1212, and any combinations thereof. Input device 1232may include a touch screen interface that may be a part of or separatefrom display 1236, discussed further below. Input device 1232 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1200 via storage device 1224 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1240. A networkinterface device, such as network interface device 1240, may be utilizedfor connecting computer system 1200 to one or more of a variety ofnetworks, such as network 1244, and one or more remote devices 1248connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1244, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1220, etc.) may be communicated to and/or fromcomputer system 1200 via network interface device 1240.

Computer system 1200 may further include a video display adapter 1252for communicating a displayable image to a display device, such asdisplay device 1236. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1252 and display device 1236 maybe utilized in combination with processor 1204 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1200 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1212 via a peripheral interface 1256.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An apparatus for encrypting externalcommunication for an electric aircraft, wherein the apparatus comprises:a communication component configured to communicate with network nodes;a battery onboard an electric aircraft and configured to power theelectric aircraft; a battery sensor connected to the battery, whereinthe battery sensor is configured to generate a battery datum, whereinthe battery datum includes information on a state of charge of thebattery; a computing device communicatively connected to thecommunication component and the battery sensor, wherein the computingdevice is configured to: receive the battery datum; encrypt the batterydatum using an encryption process; identify a network node; and transmitthe encrypted battery datum to the identified network node using thecommunication component; and a ground support associated with theidentified network node, wherein the ground support includes a chargingstation equipped to recharge the battery of the electric aircraft. 2.The apparatus of claim 1, wherein the battery datum includes anindication of one or more characteristics of the battery.
 3. Theapparatus of claim 1, wherein the electric aircraft further comprises amanned electric vertical take-off and landing aircraft.
 4. The apparatusof claim 1, wherein the computing device is configured to authenticatethe network node.
 5. The apparatus of claim 1, wherein at least anetwork node includes an inter-aircraft network node.
 6. The apparatusof claim 1, wherein at least a network node includes an intra-aircraftnetwork node.
 7. The apparatus of claim 1, wherein at least a networknode includes a non-aircraft network node.
 8. The apparatus of claim 1,wherein the computing device is configured to provide encryptedcommunication at least in part by employing digital signatures.
 9. Theapparatus of claim 1, wherein the computing device is configured toreceive communication from at least network node.
 10. The apparatus ofclaim 1, wherein the computing device is configured use a routingtechnique to send a message to a specific network node.
 11. A method ofuse for encrypting external communications for an electric aircraft,wherein the method comprises: communicating, using a communicationcomponent, with network nodes; powering, using a battery onboard anelectric aircraft, the electric aircraft; generating, using a batterysensor connected to the battery, a battery datum, wherein the batterydatum includes information on a state of charge of the battery;communicatively connecting a computing device to the communicationcomponent and the battery sensor; receiving, using the computing device,the battery datum; encrypting, using the computing device, the batterydatum using an encryption process; identifying, using the computingdevice, the network node; and transmitting the encrypted battery datumto the identified network node using the communication component,wherein a ground support is associated with the identified network node,wherein the ground support includes a charging station equipped torecharge the battery of the electric aircraft.
 12. The method of claim11, wherein the battery datum includes an indication of one or morecharacteristics of the battery.
 13. The method of claim 11, wherein theelectric aircraft further comprises a manned electric vertical take-offand landing aircraft.
 14. The method of claim 11, wherein the computingdevice is configured to authenticate the network node.
 15. The method ofclaim 11, wherein at least a network node includes an inter-aircraftnetwork node.
 16. The method of claim 11, wherein at least a networknode includes an intra-aircraft network node.
 17. The method of claim11, wherein at least a network node includes a non-aircraft networknode.
 18. The method of claim 11, wherein the computing device isconfigured to provide encrypted communication at least in part byemploying digital signatures.
 19. The method of claim 11, wherein thecomputing device is configured to receive communication from at least anetwork node.
 20. The method of claim 11, wherein the computing deviceis configured use a routing technique to send a message to a specificnetwork node.